1. Business Problem

1.1. Description

Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/

Data: Memorial Sloan Kettering Cancer Center (MSKCC)

Download training_variants.zip and training_text.zip from Kaggle.

Context:

Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/discussion/35336#198462

Problem statement :

Classify the given genetic variations/mutations based on evidence from text-based clinical literature.

1.2. Source/Useful Links

Some articles and reference blogs about the problem statement

  1. https://www.forbes.com/sites/matthewherper/2017/06/03/a-new-cancer-drug-helped-almost-everyone-who-took-it-almost-heres-what-it-teaches-us/#2a44ee2f6b25
  2. https://www.youtube.com/watch?v=UwbuW7oK8rk
  3. https://www.youtube.com/watch?v=qxXRKVompI8

1.3. Real-world/Business objectives and constraints.

  • No low-latency requirement.
  • Interpretability is important.
  • Errors can be very costly.
  • Probability of a data-point belonging to each class is needed.

2. Machine Learning Problem Formulation

2.1. Data

2.1.1. Data Overview

  • Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/data
  • We have two data files: one conatins the information about the genetic mutations and the other contains the clinical evidence (text) that human experts/pathologists use to classify the genetic mutations.
  • Both these data files are have a common column called ID

  • Data file's information:

    • training_variants (ID , Gene, Variations, Class)
    • training_text (ID, Text)

2.1.2. Example Data Point

training_variants

ID,Gene,Variation,Class
0,FAM58A,Truncating Mutations,1
1,CBL,W802*,2
2,CBL,Q249E,2
...

training_text

ID,Text
0||Cyclin-dependent kinases (CDKs) regulate a variety of fundamental cellular processes. CDK10 stands out as one of the last orphan CDKs for which no activating cyclin has been identified and no kinase activity revealed. Previous work has shown that CDK10 silencing increases ETS2 (v-ets erythroblastosis virus E26 oncogene homolog 2)-driven activation of the MAPK pathway, which confers tamoxifen resistance to breast cancer cells. The precise mechanisms by which CDK10 modulates ETS2 activity, and more generally the functions of CDK10, remain elusive. Here we demonstrate that CDK10 is a cyclin-dependent kinase by identifying cyclin M as an activating cyclin. Cyclin M, an orphan cyclin, is the product of FAM58A, whose mutations cause STAR syndrome, a human developmental anomaly whose features include toe syndactyly, telecanthus, and anogenital and renal malformations. We show that STAR syndrome-associated cyclin M mutants are unable to interact with CDK10. Cyclin M silencing phenocopies CDK10 silencing in increasing c-Raf and in conferring tamoxifen resistance to breast cancer cells. CDK10/cyclin M phosphorylates ETS2 in vitro, and in cells it positively controls ETS2 degradation by the proteasome. ETS2 protein levels are increased in cells derived from a STAR patient, and this increase is attributable to decreased cyclin M levels. Altogether, our results reveal an additional regulatory mechanism for ETS2, which plays key roles in cancer and development. They also shed light on the molecular mechanisms underlying STAR syndrome.Cyclin-dependent kinases (CDKs) play a pivotal role in the control of a number of fundamental cellular processes (1). The human genome contains 21 genes encoding proteins that can be considered as members of the CDK family owing to their sequence similarity with bona fide CDKs, those known to be activated by cyclins (2). Although discovered almost 20 y ago (3, 4), CDK10 remains one of the two CDKs without an identified cyclin partner. This knowledge gap has largely impeded the exploration of its biological functions. CDK10 can act as a positive cell cycle regulator in some cells (5, 6) or as a tumor suppressor in others (7, 8). CDK10 interacts with the ETS2 (v-ets erythroblastosis virus E26 oncogene homolog 2) transcription factor and inhibits its transcriptional activity through an unknown mechanism (9). CDK10 knockdown derepresses ETS2, which increases the expression of the c-Raf protein kinase, activates the MAPK pathway, and induces resistance of MCF7 cells to tamoxifen (6). ...

2.2. Mapping the real-world problem to an ML problem

2.2.1. Type of Machine Learning Problem

There are nine different classes a genetic mutation can be classified into => Multi class classification problem

2.2.2. Performance Metric

Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment#evaluation

Metric(s):

  • Multi class log-loss
  • Confusion matrix

2.2.3. Machine Learing Objectives and Constraints

Objective: Predict the probability of each data-point belonging to each of the nine classes.

Constraints:

  • Interpretability
  • Class probabilities are needed.
  • Penalize the errors in class probabilites => Metric is Log-loss.
  • No Latency constraints.

2.3. Train, CV and Test Datasets

Split the dataset randomly into three parts train, cross validation and test with 64%,16%, 20% of data respectively

Importing the required libraries 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly as pt
from scipy import stats
from wordcloud import STOPWORDS
from nltk import SnowballStemmer , PorterStemmer
from bs4 import BeautifulSoup
import re
import collections
import string
import math
from sklearn.preprocessing import LabelEncoder , OneHotEncoder
from sklearn.feature_extraction import FeatureHasher
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import log_loss , confusion_matrix
from sklearn.linear_model import LogisticRegression
from prettytable import PrettyTable
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler
from sklearn.naive_bayes import MultinomialNB 
from sklearn.neighbors import KNeighborsClassifier
from imblearn.combine import SMOTETomek
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import StackingClassifier

/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm
/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning:

The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning:

The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.

3. Exploratory Data Analysis

df_text=pd.read_csv("/content/drive/My Drive/training_text/training_text" ,sep="\|\|",engine="python",names=["ID","TEXT"],skiprows=1)
df_variants=pd.read_csv("/content/drive/My Drive/training_text/training_variants")

df_text.head()
ID TEXT
0 0 Cyclin-dependent kinases (CDKs) regulate a var...
1 1 Abstract Background Non-small cell lung canc...
2 2 Abstract Background Non-small cell lung canc...
3 3 Recent evidence has demonstrated that acquired...
4 4 Oncogenic mutations in the monomeric Casitas B... 
df_variants.head()
ID Gene Variation Class
0 0 FAM58A Truncating Mutations 1
1 1 CBL W802* 2
2 2 CBL Q249E 2
3 3 CBL N454D 3
4 4 CBL L399V 4 
df=pd.merge(df_text,df_variants , on= "ID")

df.head()
ID TEXT Gene Variation Class
0 0 Cyclin-dependent kinases (CDKs) regulate a var... FAM58A Truncating Mutations 1
1 1 Abstract Background Non-small cell lung canc... CBL W802* 2
2 2 Abstract Background Non-small cell lung canc... CBL Q249E 2
3 3 Recent evidence has demonstrated that acquired... CBL N454D 3
4 4 Oncogenic mutations in the monomeric Casitas B... CBL L399V 4 
df.shape

(3321, 5)
  • There are 3321 Data points and 5 cloumns 
df.columns

Index(['ID', 'TEXT', 'Gene', 'Variation', 'Class'], dtype='object')
df.groupby('Class')['ID'].count().plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x7fdb4e0f32e8>
  • We can see that the class 1,2,4,7 have failry large amount of data points while 5,6 have less count and 8,9 being very least count of data points . we can see imbalance data set here.
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 3321 entries, 0 to 3320
Data columns (total 5 columns):
 #   Column     Non-Null Count  Dtype 
---  ------     --------------  ----- 
 0   ID         3321 non-null   int64 
 1   TEXT       3316 non-null   object
 2   Gene       3321 non-null   object
 3   Variation  3321 non-null   object
 4   Class      3321 non-null   int64 
dtypes: int64(2), object(3)
memory usage: 155.7+ KB

df.Gene.describe()

count      3321
unique      264
top       BRCA1
freq        264
Name: Gene, dtype: object
  • There are 264 unique genes , and the gene name BRCAI has more frequency of occuring means it is present in that many datapoints and that gene occurs more.
df.Gene.value_counts(ascending=True)

SDHB        1
ARID1A      1
RAD51B      1
FOXO1       1
NCOR1       1
         ... 
BRCA2     125
PTEN      126
EGFR      141
TP53      163
BRCA1     264
Name: Gene, Length: 264, dtype: int64
  • We can see the most number of repeating genes are BRCAI followed by TP53 ,EGFR and so on...
df.Variation.value_counts()

Truncating Mutations    93
Deletion                74
Amplification           71
Fusions                 34
Overexpression           6
                        ..
C47G                     1
S241T                    1
X1009_splice             1
D74N                     1
T338I                    1
Name: Variation, Length: 2996, dtype: int64
  • The most common type of mutations are Truncating Mutations followed by deletion ,amplification etc.
df.Class.value_counts()

7    953
4    686
1    568
2    452
6    275
5    242
3     89
9     37
8     19
Name: Class, dtype: int64
df.head()
ID TEXT Gene Variation Class
0 0 Cyclin-dependent kinases (CDKs) regulate a var... FAM58A Truncating Mutations 1
1 1 Abstract Background Non-small cell lung canc... CBL W802* 2
2 2 Abstract Background Non-small cell lung canc... CBL Q249E 2
3 3 Recent evidence has demonstrated that acquired... CBL N454D 3
4 4 Oncogenic mutations in the monomeric Casitas B... CBL L399V 4

Looking for null values

</div> </div> </div> 
df[df.isna().any(1)]
ID TEXT Gene Variation Class
1109 1109 NaN FANCA S1088F 1
1277 1277 NaN ARID5B Truncating Mutations 1
1407 1407 NaN FGFR3 K508M 6
1639 1639 NaN FLT1 Amplification 6
2755 2755 NaN BRAF G596C 7
  • A good way to replace them will be by the gene + variation as we cannot drop them or repleace them by empty string due to our objective.
df.loc[df['TEXT'].isnull(),'TEXT'] = df['Gene'] +' '+df['Variation']

df[df.isna().any(1)]
ID TEXT Gene Variation Class
  • Before Going further for analysis lets preprocess the text data 
def decontracted(phrase):
    # specific
    phrase = re.sub(r"won't", "will not", phrase)
    phrase = re.sub(r"can\'t", "can not", phrase)
    # general
    phrase = re.sub(r"n\'t", " not", phrase)
    phrase = re.sub(r"\'re", " are", phrase)
    phrase = re.sub(r"\'s", " is", phrase)
    phrase = re.sub(r"\'d", " would", phrase)
    phrase = re.sub(r"\'ll", " will", phrase)
    phrase = re.sub(r"\'t", " not", phrase)
    phrase = re.sub(r"\'ve", " have", phrase)
    phrase = re.sub(r"\'m", " am", phrase)
    return phrase

def preProccesing( data ):

  column_data=data['TEXT']

  review_text=[ ]
  
  for sentance in column_data.values:
    #1.Removing Urls
    sentance=re.sub(r"http\S+" , "" , sentance )
    #2.Removing html tags
    sentance=re.sub(r"<[^<]+?>", "" , sentance )
    #Removing lmxl
    soup = BeautifulSoup(sentance, 'lxml')
    sentance = soup.get_text()
    #3.decontracting phares
    sentance=decontracted(sentance)
    #4.Removing word with numbers
    sentance=re.sub("S*\d\S*" , "" , sentance)
    #5.remove Special charactor punc spaces
    sentance=re.sub(r"\W+", " ", sentance)
    # replace every special char with space
    sentance = re.sub('[^a-zA-Z0-9\n]', ' ',sentance)
    # replace multiple spaces with single space
    sentance = re.sub('\s+',' ', sentance)
    
    sentance = ' '.join(e.lower() for e in sentance.split() if e.lower() not in STOPWORDS)
  
    review_text.append(sentance.strip())
  df['Cleaned_text'] = pd.DataFrame(review_text)

Lets Run this function

</div> </div> </div> 
preProccesing(df)

df.head()
ID TEXT Gene Variation Class Cleaned_text
0 0 Cyclin-dependent kinases (CDKs) regulate a var... FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 Abstract Background Non-small cell lung canc... CBL W802* 2 abstract background non small cell lung cancer...
2 2 Abstract Background Non-small cell lung canc... CBL Q249E 2 abstract background non small cell lung cancer...
3 3 Recent evidence has demonstrated that acquired... CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 Oncogenic mutations in the monomeric Casitas B... CBL L399V 4 oncogenic mutations monomeric casitas b lineag...

*As we got the cleaned text lets remove the TEXT field from the DataFrame

df=df.drop(columns='TEXT')

df.head()
ID Gene Variation Class Cleaned_text
0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 CBL W802* 2 abstract background non small cell lung cancer...
2 2 CBL Q249E 2 abstract background non small cell lung cancer...
3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... 
df[df.isna().any(axis=1)]
ID Gene Variation Class Cleaned_text 
df.to_csv("OriginalDFWithCleanedTextPresent.csv")

Original_df=df

df.Class.value_counts()

7    953
4    686
1    568
2    452
6    275
5    242
3     89
9     37
8     19
Name: Class, dtype: int64
  • lets perform Univariate analysis on these features

3.1 Univariate Analysis

</p> </div> </div> </div>

3.1.2 Analysing Gene Feature

</p> </div> </div> </div>

Q1. what type of feature is gene ?

</div> </div> </div>
  • It is a Categorical Feature

Q2.How many Categories are there?

</div> </div> </div> 
df.groupby("Gene")["ID"].count()

Gene
ABL1       26
ACVR1       3
AGO2        5
AKT1       28
AKT2       11
           ..
WHSC1       1
WHSC1L1     1
XPO1        2
XRCC2       2
YAP1        4
Name: ID, Length: 264, dtype: int64
  • we can see that there are 263 unique Gene categories are present

Q3. Distribution of Gene?

</div> </div> </div> 
unique_genes = df['Gene'].value_counts()
# the top 10 genes that occured most
print(unique_genes.head(10))

BRCA1     264
TP53      163
EGFR      141
PTEN      126
BRCA2     125
KIT        99
BRAF       93
ALK        69
ERBB2      69
PDGFRA     60
Name: Gene, dtype: int64

sums=sum(unique_genes.values)

result=unique_genes.values/sums

plt.plot(result,label="Histrogram of Genes")
plt.xlabel('Index of a Gene')
plt.ylabel('Number of Occurances')
plt.legend()
plt.grid()
plt.show()
  • There are some genes which occur very less , some genes which occur more
plt.plot(np.cumsum(result),label='Cumulative distribution of Genes')
plt.grid()
plt.legend()
plt.show()
  • 80 Percent of the genes have index in range 50-75

Q4 How to featurize this Feature? Gene

</div> </div> </div>

1.One-Hot Encoding

2.Mean-ResponseCoding

3.FeatureHashing

source:-https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher

</div> </div> </div>
  • Lets Create data frames to work with this 
df_one_hot_encoding=df
df_mean_response_coding=df
df_featureHashing=df
  • OneHotEncoding
df_one_hot_encoding=df_one_hot_encoding.drop(columns=['ID', 'Variation','Class','Cleaned_text'])

df_one_hot_encoding.head()
Gene
0 FAM58A
1 CBL
2 CBL
3 CBL
4 CBL 
labelencode=LabelEncoder()

df_one_hot_encoding['Generated_lables' ]=labelencode.fit_transform(df_one_hot_encoding['Gene'])

df_one_hot_encoding
Gene Generated_lables
0 FAM58A 85
1 CBL 39
2 CBL 39
3 CBL 39
4 CBL 39
... ... ...
3316 RUNX1 221
3317 RUNX1 221
3318 RUNX1 221
3319 RUNX1 221
3320 RUNX1 221

3321 rows × 2 columns

list_feature_labels=list(labelencode.classes_) 

df_one_hot_encoding.head()
Gene Generated_lables
0 FAM58A 85
1 CBL 39
2 CBL 39
3 CBL 39
4 CBL 39 
onehotencoder=OneHotEncoder()

array_generated_onehotfeatures=onehotencoder.fit_transform(df_one_hot_encoding[['Generated_lables']]).toarray()

data_gen= pd.DataFrame(array_generated_onehotfeatures,columns=list_feature_labels)

data_gen.head()
ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... SDHB SDHC SETD2 SF3B1 SHOC2 SHQ1 SMAD2 SMAD3 SMAD4 SMARCA4 SMARCB1 SMO SOS1 SOX9 SPOP SRC SRSF2 STAG2 STAT3 STK11 TCF3 TCF7L2 TERT TET1 TET2 TGFBR1 TGFBR2 TMPRSS2 TP53 TP53BP1 TSC1 TSC2 U2AF1 VEGFA VHL WHSC1 WHSC1L1 XPO1 XRCC2 YAP1
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5 rows × 264 columns

onehotencoded_features_gene=pd.concat([df_one_hot_encoding,data_gen , df['Class']] , axis=1 )

onehotencoded_features_gene.Class.value_counts()

7    953
4    686
1    568
2    452
6    275
5    242
3     89
9     37
8     19
Name: Class, dtype: int64
finalOneHotEncodedFeaturesOfGene=onehotencoded_features_gene.drop(columns=['Gene','Generated_lables'])

finalOneHotEncodedFeaturesOfGene.Class.value_counts()

7    953
4    686
1    568
2    452
6    275
5    242
3     89
9     37
8     19
Name: Class, dtype: int64
  • As we can see now we have one hot encodeed features of our gene features as dataframe(finalOneHotEncodedFeaturesOfGene).
finalOneHotEncodedFeaturesOfGene.to_csv("finalOneHotEncodedFeaturesOfGene.csv")
  • Feature Hashing for GeneFeature

We had 263 categories of genes so we can reduce them to half or less our wish better we reduce them to size of 9 As we have 9 classes.

df_featureHashing.head()
ID Gene Variation Class Cleaned_text
0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 CBL W802* 2 abstract background non small cell lung cancer...
2 2 CBL Q249E 2 abstract background non small cell lung cancer...
3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... 
df_featureHashing=df_featureHashing.drop(columns=['ID','Variation','Class','Cleaned_text'])

df_featureHashing.head()
Gene
0 FAM58A
1 CBL
2 CBL
3 CBL
4 CBL 
hasher=FeatureHasher(n_features=9,input_type='string')

hased_features=hasher.fit_transform(df_featureHashing['Gene']).toarray()

dataframe_hashed_features=pd.DataFrame(hased_features)

dataframe_hashed_features.head()
0 1 2 3 4 5 6 7 8
0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0
1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0
2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0
3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0
4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 
finalFeatureHashedFeaturesOfGene=pd.concat([dataframe_hashed_features,df['Class']],axis=1)

finalFeatureHashedFeaturesOfGene.Class.value_counts()

7    953
4    686
1    568
2    452
6    275
5    242
3     89
9     37
8     19
Name: Class, dtype: int64
finalFeatureHashedFeaturesOfGene.to_csv("finalFeatureHashedFeaturesOfGene.csv")
  • Now Lets go for meanResponseCoding
df_mean_response_coding.head()
ID Gene Variation Class Cleaned_text
0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 CBL W802* 2 abstract background non small cell lung cancer...
2 2 CBL Q249E 2 abstract background non small cell lung cancer...
3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... 
df_mean_response_coding.Gene.value_counts()

BRCA1     264
TP53      163
EGFR      141
PTEN      126
BRCA2     125
         ... 
DUSP4       1
TCF7L2      1
VEGFA       1
ASXL1       1
ARID1A      1
Name: Gene, Length: 264, dtype: int64
df_mean_response_coding=df_mean_response_coding.drop(columns=['Variation','Cleaned_text'])

df_mean_response_coding.Gene.value_counts()

BRCA1     264
TP53      163
EGFR      141
PTEN      126
BRCA2     125
         ... 
IKZF1       1
GNA11       1
INPP4B      1
FGF3        1
ARID1A      1
Name: Gene, Length: 264, dtype: int64
df.head()
ID Gene Variation Class Cleaned_text
0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 CBL W802* 2 abstract background non small cell lung cancer...
2 2 CBL Q249E 2 abstract background non small cell lung cancer...
3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... 
len(df)

3321
dummy=df_mean_response_coding[0:3321]

objects=dummy.Gene.value_counts()

def myfunction_one(data):
  dummy1=data
  my_dicti={}


  for i,denominator in objects.items():
    vector=[]
    for k in range (1,10):
      
      count=len(dummy1.loc[(dummy1['Gene']==i) & (dummy1['Class']==k)])
      vector.append( (count + (1*10)) / (denominator + (1*90))  )
    
    my_dicti[i] = vector
  return my_dicti

my_diciti=myfunction_one(dummy)

def vectoresCreation(data):
  dummy1=data  
  eachrow_vector_data=[ ]
  for index,row in dummy1.iterrows():
    if row["Gene"] in dict(dummy1.Gene.value_counts()).keys():
      eachrow_vector_data.append(my_diciti[row["Gene"]])
    else :
      eachrow_vector_data.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])  

  return(eachrow_vector_data)

eachRow_Vector=vectoresCreation(dummy)

df_meanresponse_vectors=pd.DataFrame(eachRow_Vector)

df_meanresponse_vectors.shape

(3321, 9)
df_meanresponse_vectors=pd.concat([df_meanresponse_vectors , df["Class"]] ,axis=1)

df_meanresponse_vectors.Class.value_counts()

7    953
4    686
1    568
2    452
6    275
5    242
3     89
9     37
8     19
Name: Class, dtype: int64
df_meanresponse_vectors.to_csv("FinalMeanResponseVectorsOfGene.csv")

Q5.How good is this gene feature in predicting y_i?

We can do this in many ways one is by simple plotting and other by simple models.

  • Lets perform simple model on our three vectors of features we have lets statrt with hashed features as the features are small we can take knn,svm,dt ,, which works really well when we have less number of features and try svms as well.
finalFeatureHashedFeaturesOfGene=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")

finalFeatureHashedFeaturesOfGene=finalFeatureHashedFeaturesOfGene.drop(columns='Unnamed: 0')

finalFeatureHashedFeaturesOfGene.head()
0 1 2 3 4 5 6 7 8 Class
0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 1
1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2
2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2
3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 3
4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 4 
finalFeatureHashedFeaturesOfGene.shape

(3321, 10)
finalFeatureHashedFeaturesOfGene.Class.value_counts()  

7    953
4    686
1    568
2    452
6    275
5    242
3     89
9     37
8     19
Name: Class, dtype: int64
y_true_Hashed=finalFeatureHashedFeaturesOfGene['Class'].values
X_Hashed=finalFeatureHashedFeaturesOfGene[["0","1","2","3","4","5","6","7","8"]]

model to test

def SVM(var1,var2):
  """
  This function is use to build n hyperparamater tune SVM
  """
  X = var1
  y_true = var2

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print ( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  #Svm HyperTuning
  logLoss_train=[ ]
  logLoss_cv=[ ]
  c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
  for i in c:
        clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
        clf_SGD.fit(x_train,y_train)
        
        calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
        calibrated.fit(x_train , y_train)
        
        predict_y = calibrated.predict_proba(x_train)
        predict_y2 = calibrated.predict_proba(x_cv)
        
        logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
        
        logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
        
        

        

        

        
    #plt.plot((c), train_auc, label='Train AUC')
    #plt.plot((c), cv_auc, label='CV AUC')

  plt.plot(np.log(c), logLoss_train, label='Train logloss')
  plt.plot(np.log(c), logLoss_cv, label='CV logloss')

  plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
  plt.legend()

  plt.xlabel("C: hyperparameter")
  plt.ylabel("LogLoss")
  plt.title("ERROR PLOTS")
  return(plt.show())



  

SVM(X_Hashed,y_true_Hashed)

(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)

by seeing the above plot we take alpha as 0.001

def SVMModel(var1 , var2):




  X = var1
  y_true = var2

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)
  
  logLoss_test=[ ]
  logLoss_cv_new=[ ]
  logLoss_train_new=[ ]
  
  
  clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=0.001,class_weight='balanced')
  clf_SGD_test.fit(x_train,y_train)

  calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
  calibrated.fit(x_train , y_train)
  
  predict_y_test = calibrated.predict_proba(x_test)
  logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
  
  predicted_y_train=calibrated.predict_proba(x_train)
  logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))

  predicted_y_cv=calibrated.predict_proba(x_cv)
  logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
  

  print("The Logg loss for training data with best aplha {} is {} ".format( 0.001 ,logLoss_train_new ))
  print("The Logg loss for cv data with best aplha {} is {}".format( 0.001,logLoss_cv_new ))  
  print("The Logg loss for test data with best aplha {} is {}".format( 0.001,logLoss_test))

SVMModel(X_Hashed,y_true_Hashed)

(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
The Logg loss for training data with best aplha 0.001 is [1.7364535644919004] 
The Logg loss for cv data with best aplha 0.001 is [1.7737982623403665]
The Logg loss for test data with best aplha 0.001 is [1.7470137046208585]
  • we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well

lets go to meanresponse coding features and apply SVM

finalMeanResponseVectorsOfGene=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")

finalMeanResponseVectorsOfGene=finalMeanResponseVectorsOfGene.drop(columns='Unnamed: 0')

finalMeanResponseVectorsOfGene.columns

Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object')
x_meanResponse=finalMeanResponseVectorsOfGene[['0','1','2','3','4','5','6','7','8']]
y_true_meanResponse=finalMeanResponseVectorsOfGene.Class.values

def SVM_meanResponseCoding(var1,var2):
  """
  This function is use to build n hyperparamater tune SVM
  """
  X = var1
  y_true = var2

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print ( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  #Svm HyperTuning
  logLoss_train=[ ]
  logLoss_cv=[ ]
  c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
  for i in c:
        clf=SVC(C=i,kernel='rbf',class_weight='balanced',probability=True)
        #clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
        clf.fit(x_train,y_train)
        
        calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
        calibrated.fit(x_train , y_train)
        
        predict_y = calibrated.predict_proba(x_train)
        predict_y2 = calibrated.predict_proba(x_cv)
        
        logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
        
        logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
        
        

        

        

        
    #plt.plot((c), train_auc, label='Train AUC')
    #plt.plot((c), cv_auc, label='CV AUC')

  plt.plot(np.log(c), logLoss_train, label='Train logloss')
  plt.plot(np.log(c), logLoss_cv, label='CV logloss')

  plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
  plt.legend()

  plt.xlabel("C: hyperparameter")
  plt.ylabel("LogLoss")
  plt.title("ERROR PLOTS")
  return(plt.show())



  

SVM_meanResponseCoding(x_meanResponse,y_true_meanResponse)

(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)

We ca see that i can take c = 1 from above graph Lets go for testing on test data

def SVM_meanResponseCoding_test(var1,var2):
  """
  This function is use to build n test SVM
  """
  X = var1
  y_true = var2

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print ( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  
  logLoss_train=[ ]
  logLoss_cv=[ ]
  logLoss_test=[ ]
  
  clf=SVC(C=1,kernel='rbf',class_weight='balanced',probability=True)
        #clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
  clf.fit(x_train,y_train)
        
  calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
  calibrated.fit(x_train , y_train)
        
  predict_y = calibrated.predict_proba(x_train)
  predict_y2 = calibrated.predict_proba(x_cv)
  predict_y3 =calibrated.predict_proba(x_test)
        
  logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
        
  logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))

  logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15))
  
  print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train))
  print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv ))  
  print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))

        
        

        

        




  

SVM_meanResponseCoding_test(x_meanResponse,y_true_meanResponse)

(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
The Logg loss for training data with best aplha 1 is [1.1284981651581154] 
The Logg loss for cv data with best aplha 1 is [1.1436551747978385]
The Logg loss for test data with best aplha 1 is [1.1082205650636348]
  • we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well.

comming to Onehotencodded Features we can use logistic regression due to high dimentions

finalOneHotFeatures=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")

finalOneHotFeatures=finalOneHotFeatures.drop(columns="Unnamed: 0")

finalOneHotFeatures.columns

Index(['ABL1', 'ACVR1', 'AGO2', 'AKT1', 'AKT2', 'AKT3', 'ALK', 'APC', 'AR',
       'ARAF',
       ...
       'TSC2', 'U2AF1', 'VEGFA', 'VHL', 'WHSC1', 'WHSC1L1', 'XPO1', 'XRCC2',
       'YAP1', 'Class'],
      dtype='object', length=265)
x_oneHot=finalOneHotFeatures.iloc[:,0:264 ]

y_true_oneHot=finalOneHotFeatures.Class.values

Model

def LogisticReg_tune(var1,var2):
  """
  This Function is used to tune the logistic regression
  """
  
  X=var1
  y_true=var2

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print ( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)



  logLoss_train = []
  logLoss_cv = []
  c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 ]
  for i in c:
      clf = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
      clf.fit(x_train , y_train)
     

      
      calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
      calibrated.fit(x_train , y_train)
        
      predict_y = calibrated.predict_proba(x_train)
      predict_y2 = calibrated.predict_proba(x_cv)
        
      logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
        
      logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
        
        

        

        

        
    #plt.plot((c), train_auc, label='Train AUC')
    #plt.plot((c), cv_auc, label='CV AUC')

  plt.plot(np.log(c), logLoss_train, label='Train logloss')
  plt.plot(np.log(c), logLoss_cv, label='CV logloss')

  plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
  plt.legend()

  plt.xlabel("C: hyperparameter")
  plt.ylabel("LogLoss")
  plt.title("ERROR PLOTS")
  return(plt.show())



  

LogisticReg_tune(x_oneHot,y_true_oneHot)

(2124, 264) (2124,)
(532, 264) (532,)
(665, 264) (665,)
  • by seeing the above graph i can take log loss as 0.1 .
def LogisticReg_test(var1,var2):
  """
  This Function is used to tune the logistic regression
  """
  
  X=var1
  y_true=var2

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print ( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)



  logLoss_train = []
  logLoss_cv = [ ]
  logLoss_test=[ ]
  clf = LogisticRegression(penalty='l2',C= 0.1,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
  clf.fit(x_train , y_train)
      

      
  calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
  calibrated.fit(x_train , y_train)
        
  predict_y = calibrated.predict_proba(x_train)
  predict_y2 = calibrated.predict_proba(x_cv)
  predict_y3 = calibrated.predict_proba(x_test)     
  
  logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
  logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
  logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15))      

  print("The Logg loss for training data with best aplha {} is {} ".format( 0.1 ,logLoss_train))
  print("The Logg loss for cv data with best aplha {} is {}".format( 0.1,logLoss_cv ))  
  print("The Logg loss for test data with best aplha {} is {}".format( 0.1,logLoss_test))

  
  

LogisticReg_test(x_oneHot,y_true_oneHot)

(2124, 264) (2124,)
(532, 264) (532,)
(665, 264) (665,)
The Logg loss for training data with best aplha 0.1 is [1.2638620900916107] 
The Logg loss for cv data with best aplha 0.1 is [1.3524898055298527]
The Logg loss for test data with best aplha 0.1 is [1.3393455550993199]
  • we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well.
  • Observations on Gene Featurisations
table=PrettyTable()

table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]

table.add_row(["Gene","FeatureHaser","SVM-kernel-RBF",0.001,1.7364,1.7737,1.7470])
table.add_row(["Gene","MeanResponseCoding","SVM-kernel-RBF",1,1.1284,1.1436,1.1082])
table.add_row(["Gene","OneHotEncoding","LogisticRegression",0.1,1.2638,1.3524,1.3393])
print(table)

+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
| Feature Name | Featurisation Used |       Model        | HyperParameter |  train Logloss |  Cv Logloss | test Logloss |
+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
|     Gene     |    FeatureHaser    |   SVM-kernel-RBF   |     0.001      |     1.7364     |    1.7737   |    1.747     |
|     Gene     | MeanResponseCoding |   SVM-kernel-RBF   |       1        |     1.1284     |    1.1436   |    1.1082    |
|     Gene     |   OneHotEncoding   | LogisticRegression |      0.1       |     1.2638     |    1.3524   |    1.3393    |
+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+

SVM with less dimensions with meanResponseCoding worked well because it uses kernal rbf, it would have worked also with hashed features as welldue to rbf kernel.

Answer to Question 5 would be how gene is useful in our objective, from results (logloss) we obtained i can say that it is useful feature in our classification model.

Q6. Is the Gene feature stable across all the data sets (Test, Train, Cross validation)?

Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.

3.1.3 Analysing of Variation Feature

</p> </div> </div> </div> 
df.head()
ID Gene Variation Class Cleaned_text
0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 CBL W802* 2 abstract background non small cell lung cancer...
2 2 CBL Q249E 2 abstract background non small cell lung cancer...
3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag...

Q1. what is the Variation Feature Type?

</div> </div> </div>

It is Categorical

</div> </div> </div>

Q2.How many categories are present?

</div> </div> </div> 
len(df.Variation.value_counts())

2996

There are 2996 categories

</div> </div> </div>

Q3.What is the distribution of categories?

</div> </div> </div> 
unique_variations=df.Variation.value_counts()

sum0f_unique=sum(unique_variations)

histoGram_variation=unique_variations.values / sum0f_unique

plt.plot(histoGram_variation ,label = "Histogram of Variation")
plt.xlabel(" index of Variation")
plt.ylabel(" count")
plt.legend()
plt.grid()
plt.show()
  • by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0.
cumsum_histogram=np.cumsum(histoGram_variation)
plt.plot(cumsum_histogram,label='Cumulative distribution of variations')
plt.grid()
plt.legend()
plt.show()

Q4.How to Featurise this variation Feature?

</div> </div> </div>

There are few ways:-

1.OneHotEncoding

2.FeatureHasher

3.MeanResponseCoding

</div> </div> </div>
  • Lets Start with OneHotEncoding of VariationFeature
ds_oneHotencoding=df
ds_meanResponse_coding=df
ds_featureHasher=df

ds_oneHotencoding.columns

Index(['ID', 'Gene', 'Variation', 'Class', 'Cleaned_text'], dtype='object')
ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text'])

ds_oneHotencoding.head()
Variation
0 Truncating Mutations
1 W802*
2 Q249E
3 N454D
4 L399V 
label_encoder=LabelEncoder()
ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"])

ds_oneHotencoding
Variation Labelencodedvalues
0 Truncating Mutations 2629
1 W802* 2856
2 Q249E 1897
3 N454D 1667
4 L399V 1447
... ... ...
3316 D171N 306
3317 A122* 28
3318 Fusions 807
3319 R80C 2249
3320 K83E 1333

3321 rows × 2 columns

featurenames=label_encoder.classes_

ds_oneHotencoding
Variation Labelencodedvalues
0 Truncating Mutations 2629
1 W802* 2856
2 Q249E 1897
3 N454D 1667
4 L399V 1447
... ... ...
3316 D171N 306
3317 A122* 28
3318 Fusions 807
3319 R80C 2249
3320 K83E 1333

3321 rows × 2 columns

onehotencoder_Variation=OneHotEncoder()

encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray()

encoded_array

array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]])
ds_dash=pd.DataFrame(encoded_array , columns=featurenames)

ds_dash.head()
1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insGLYVDFREYEY Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5 rows × 2996 columns

OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1)

OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues'])

OneHotEncoded_ds.head()
1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4

5 rows × 2997 columns

OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv")

OneHotEncoded_ds
1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1
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2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4
3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1
3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1
3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4
3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4

3321 rows × 2997 columns

  • Lets create feature hasher for variation features
ds_featureHasher.head()
ID Gene Variation Class Cleaned_text
0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 CBL W802* 2 abstract background non small cell lung cancer...
2 2 CBL Q249E 2 abstract background non small cell lung cancer...
3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... 
ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"])

ds_featureHasher.head()
Variation Class
0 Truncating Mutations 1
1 W802* 2
2 Q249E 2
3 N454D 3
4 L399V 4 
hasher= FeatureHasher(n_features=9,input_type='string')

array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray()

ds_dash_hashed=pd.DataFrame(array_hashed)

hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1)

hashedEncodedFeatureof_variation.head()
0 1 2 3 4 5 6 7 8 Class
0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1
1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2
2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2
3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3
4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 
hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv")
  • Lets go for mean responsecoding of variation feature
df_mean_response_coding.head()
ID Gene Variation Class Cleaned_text
0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 CBL W802* 2 abstract background non small cell lung cancer...
2 2 CBL Q249E 2 abstract background non small cell lung cancer...
3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... 
objects_ThisFeature=df_mean_response_coding.Variation.value_counts()

objects_ThisFeature

Truncating Mutations    93
Deletion                74
Amplification           71
Fusions                 34
Overexpression           6
                        ..
K78A                     1
A1789S                   1
T401I                    1
K550_V555delinsI         1
R420H                    1
Name: Variation, Length: 2996, dtype: int64
my_dictionary_varFeature={ }


for feature_name , feature_total_count in objects_ThisFeature.items():
  
  vector_array_features=[ ]
  
  for index in range(1,10):

    count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)])

    vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90)))

  my_dictionary_varFeature[feature_name] =  vector_array_features

my_dictionary_varFeature

{'Truncating Mutations': [0.5409836065573771,
  0.060109289617486336,
  0.0546448087431694,
  0.060109289617486336,
  0.0546448087431694,
  0.06557377049180328,
  0.0546448087431694,
  0.0546448087431694,
  0.0546448087431694],
 'Deletion': [0.40853658536585363,
  0.06097560975609756,
  0.06097560975609756,
  0.15853658536585366,
  0.06097560975609756,
  0.06707317073170732,
  0.06097560975609756,
  0.06097560975609756,
  0.06097560975609756],
 'Amplification': [0.062111801242236024,
  0.19254658385093168,
  0.062111801242236024,
  0.062111801242236024,
  0.062111801242236024,
  0.12422360248447205,
  0.3105590062111801,
  0.062111801242236024,
  0.062111801242236024],
 'Fusions': [0.0967741935483871,
  0.33064516129032256,
  0.08064516129032258,
  0.08064516129032258,
  0.08064516129032258,
  0.08064516129032258,
  0.08064516129032258,
  0.08870967741935484,
  0.08064516129032258],
 'Overexpression': [0.10416666666666667,
  0.125,
  0.10416666666666667,
  0.10416666666666667,
  0.10416666666666667,
  0.10416666666666667,
  0.14583333333333334,
  0.10416666666666667,
  0.10416666666666667],
 'G12V': [0.10638297872340426,
  0.10638297872340426,
  0.10638297872340426,
  0.10638297872340426,
  0.10638297872340426,
  0.10638297872340426,
  0.14893617021276595,
  0.10638297872340426,
  0.10638297872340426],
 'Q61H': [0.10752688172043011,
  0.11827956989247312,
  0.10752688172043011,
  0.10752688172043011,
  0.10752688172043011,
  0.10752688172043011,
  0.12903225806451613,
  0.10752688172043011,
  0.10752688172043011],
 'Q61L': [0.10752688172043011,
  0.10752688172043011,
  0.10752688172043011,
  0.10752688172043011,
  0.10752688172043011,
  0.10752688172043011,
  0.13978494623655913,
  0.10752688172043011,
  0.10752688172043011],
 'E17K': [0.10752688172043011,
  0.10752688172043011,
  0.10752688172043011,
  0.10752688172043011,
  0.10752688172043011,
  0.10752688172043011,
  0.13978494623655913,
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 ...}
length_df=len(df_mean_response_coding)

array_of_vectorss=[ ]

for i in range(0,length_df):
  
  if df_mean_response_coding.iloc[i]['Variation'] in my_dictionary_varFeature.keys():
    featurename=df_mean_response_coding.iloc[i]['Variation']
    
    

    array_of_vectorss.append( my_dictionary_varFeature[featurename] )

  else:
    array_of_vectorss.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])    


print(array_of_vectorss)

[[0.5409836065573771, 0.060109289617486336, 0.0546448087431694, 0.060109289617486336, 0.0546448087431694, 0.06557377049180328, 0.0546448087431694, 0.0546448087431694, 0.0546448087431694], [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.40853658536585363, 0.06097560975609756, 0.06097560975609756, 0.15853658536585366, 0.06097560975609756, 0.06707317073170732, 0.06097560975609756, 0.06097560975609756, 0.06097560975609756], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 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0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.5409836065573771, 0.060109289617486336, 0.0546448087431694, 0.060109289617486336, 0.0546448087431694, 0.06557377049180328, 0.0546448087431694, 0.0546448087431694, 0.0546448087431694], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.062111801242236024, 0.19254658385093168, 0.062111801242236024, 0.062111801242236024, 0.062111801242236024, 0.12422360248447205, 0.3105590062111801, 0.062111801242236024, 0.062111801242236024], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989]]

meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss)

meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1)

meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv")

As we have all the vector forms of the featurisations lets proceed with further analysis

Q5.How good is this Variation feature in predicting y_i?

Lets Build simple models to check our selves

  • As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library.

1.meanResponse coded vectors for variation feature

We will use SVM rbf kernel and see the performance

</div> </div> </div>

SVM Model

meanResponseCoding_variationFeature.columns

Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 'Class'], dtype='object')
x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values
y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values

x_true_meanresponse

array([[0.54098361, 0.06010929, 0.05464481, ..., 0.05464481, 0.05464481,
        0.05464481],
       [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011,
        0.10989011],
       [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011,
        0.10989011],
       ...,
       [0.09677419, 0.33064516, 0.08064516, ..., 0.08064516, 0.08870968,
        0.08064516],
       [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011,
        0.10989011],
       [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011,
        0.10989011]])
def svm_model_tuning(var1,var2):
  """
  This function is used to create SVM and tune it
  """
  x_true=var1
  y_true=var2


  x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)



  
  print("The shape of the train n test vector as follows:")
  print(x_train.shape,y_train.shape)
  print(x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  log_loss_train=[]
  log_loss_cv=[]
  log_loss_test=[]

  hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10  ]

  for i in hyperparameter:
    
    model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced')
    model.fit(x_train,y_train)
    
    clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
    clf.fit(x_train,y_train)

    pred_ytrain=clf.predict_proba(x_train)
    log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))

    pred_ycv=clf.predict_proba(x_cv)
    log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))

  plt.plot(np.log(hyperparameter) , log_loss_train)
  plt.plot(np.log(hyperparameter), log_loss_cv)
  plt.scatter(np.log(hyperparameter),log_loss_cv)
  plt.title("Loggloss vs hyper paramenter")
  plt.xlabel("hyperparameter(C)")
  plt.ylabel("Log loss")
  plt.grid()
  return(plt.show())

svm_model_tuning(x_true_meanresponse , y_true_meanResponse)

The shape of the train n test vector as follows:
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)

From above graph i select my best C as 1

Testing of the model

def svm_model_testing(var1,var2):
  """
  This function is used to create SVM and tune it
  """
  x_true=var1
  y_true=var2


  x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)



  
  print("The shape of the train n test vector as follows:")
  print(x_train.shape,y_train.shape)
  print(x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  log_loss_train=[]
  log_loss_cv=[]
  log_loss_test=[]


  model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced')
  model.fit(x_train,y_train)
  
  clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
  clf.fit(x_train,y_train)

  pred_ytrain=clf.predict_proba(x_train)
  log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))

  pred_ycv=clf.predict_proba(x_cv)
  log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))

  pred_ytest=clf.predict_proba(x_test)
  log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))


  print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
  print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
  print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))

  

svm_model_testing(x_true_meanresponse,y_true_meanResponse)

The shape of the train n test vector as follows:
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
The Logloss for 1 the coresponding train loss is [0.13402600125222605]
The Logloss for 1 the coresponding cv loss is [0.1479095316786708]
The Logloss for 1 the coresponding test loss is [0.12267536305037746]
  • we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well

2.Hashed coded vectors for variation feature

We will use SVM rbf kernel and see the performance

</div> </div> </div> 
hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")

hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0')
hashedEncodedFeatureof_variation.head()
0 1 2 3 4 5 6 7 8 Class
0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1
1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2
2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2
3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3
4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 
x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values
y_hashed_true=hashedEncodedFeatureof_variation['Class'].values

We will reuse the same svm functions we defiend earilier

svm_model_tuning(x_hashed_true,y_hashed_true)

The shape of the train n test vector as follows:
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)

from the above graph i can choose my C as 0.0001

def svm_model_testing(var1,var2):
  """
  This function is used to create SVM and tune it
  """
  x_true=var1
  y_true=var2


  x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)



  
  print("The shape of the train n test vector as follows:")
  print(x_train.shape,y_train.shape)
  print(x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  log_loss_train=[]
  log_loss_cv=[]
  log_loss_test=[]


  model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced')
  model.fit(x_train,y_train)
  
  clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
  clf.fit(x_train,y_train)

  pred_ytrain=clf.predict_proba(x_train)
  log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))

  pred_ycv=clf.predict_proba(x_cv)
  log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))

  pred_ytest=clf.predict_proba(x_test)
  log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))


  print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train ))
  print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv ))
  print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test ))

  

svm_model_testing(x_hashed_true,y_hashed_true)

The shape of the train n test vector as follows:
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
The Logloss for 0.0001 the coresponding train loss is [1.7335822874941234]
The Logloss for 0.0001 the coresponding cv loss is [1.753463388538884]
The Logloss for 0.0001 the coresponding test loss is [1.7599055604165221]
  • we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well

3.OneHotencoding of variation feature

As this has high dimention lets use logistic regression

</div> </div> </div> 
oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0')
oneHotEncodedfeaturesof_Variation.head()
1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4

5 rows × 2997 columns

oneHotEncodedfeaturesof_Variation.columns

Index(['1_2009trunc', '2010_2471trunc', '256_286trunc', '3' Deletion',
       '385_418del', '422_605trunc', '533_534del', '534_536del', '550_592del',
       '560_561insER',
       ...
       'Y901C', 'Y931C', 'Y98H', 'Y98N', 'YAP1-FAM118B Fusion',
       'YAP1-MAMLD1 Fusion', 'ZC3H7B-BCOR Fusion', 'ZNF198-FGFR1 Fusion',
       'p61BRAF', 'Class'],
      dtype='object', length=2997)
x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values

y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values

LogisticModel

def logistic_tune(var1,var2):
  """
  This function is used to create model and tune it to find best hyper parameter
  """
  x_true=var1
  y_true=var2


  x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)



  
  print("The shape of the train n test vector as follows:")
  print(x_train.shape,y_train.shape)
  print(x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  log_loss_train=[]
  log_loss_cv=[]
  log_loss_test=[]

  hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10  ]



  for i in hyperparameter:
      
    model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
    model.fit(x_train , y_train)
      
    
    clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
    clf.fit(x_train,y_train)

    pred_ytrain=clf.predict_proba(x_train)
    log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))

    pred_ycv=clf.predict_proba(x_cv)
    log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))

  plt.plot(np.log(hyperparameter) , log_loss_train)
  plt.plot(np.log(hyperparameter), log_loss_cv)
  plt.scatter(np.log(hyperparameter),log_loss_cv)
  plt.title("Loggloss vs hyper paramenter")
  plt.xlabel("hyperparameter(C)")
  plt.ylabel("Log loss")
  plt.grid()
  return(plt.show())



  

logistic_tune(x_onehot_true,y_onehot_true)

The shape of the train n test vector as follows:
(2124, 2996) (2124,)
(532, 2996) (532,)
(665, 2996) (665,)

From the above graph i can choos aplha as 0.01

Testing model

def logistic_test(var1,var2):
  """
  This function is used to create model and tune it to find best hyper parameter
  """
  x_true=var1
  y_true=var2


  x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)



  
  print("The shape of the train n test vector as follows:")
  print(x_train.shape,y_train.shape)
  print(x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  log_loss_train=[]
  log_loss_cv=[]
  log_loss_test=[]

  
  model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
  model.fit(x_train , y_train)
      
  
  clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
  clf.fit(x_train,y_train)

  pred_ytrain=clf.predict_proba(x_train)
  log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))

  pred_ycv=clf.predict_proba(x_cv)
  log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))

  pred_ytest=clf.predict_proba(x_test)
  log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))



  print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train ))
  print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv ))
  print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test ))

logistic_test(x_onehot_true,y_onehot_true)

The shape of the train n test vector as follows:
(2124, 2996) (2124,)
(532, 2996) (532,)
(665, 2996) (665,)
The Logloss for 0.01 the coresponding train loss is [1.4238762684713966]
The Logloss for 0.01 the coresponding cv loss is [1.744474382727282]
The Logloss for 0.01 the coresponding test loss is [1.7380242209450858]
  • we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets compare all of them
  • Lets compare all observations on Variation Feature
table=PrettyTable()

table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]

table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599])
table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479])
table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380])
print(table)

+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
| Feature Name | Featurisation Used |       Model        | HyperParameter |  train Logloss |  Cv Logloss | test Logloss |
+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
|  Variation   |    FeatureHaser    |   SVM-kernel-RBF   |     0.0001     |     1.7335     |    1.7534   |    1.7599    |
|  Variation   | MeanResponseCoding |   SVM-kernel-RBF   |       1        |     0.134      |    0.1479   |    0.1479    |
|  Variation   |   OneHotEncoding   | LogisticRegression |      0.01      |     1.4238     |    1.7444   |    1.738     |
+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
  • From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well.

Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)?

Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.

3.1.4 Analysing of Text Feature

</p> </div> </div> </div>

Q1.What kind of feature is Text

</div> </div> </div>

It is sentance corpus consisting of many sentances.

Q2.how can we featurise it

There are many ways we will work with these two 1.BOW 2.TFIDF

</div> </div> </div>

1.Featurising with bow

df.head()
ID Gene Variation Class Cleaned_text
0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 CBL W802* 2 abstract background non small cell lung cancer...
2 2 CBL Q249E 2 abstract background non small cell lung cancer...
3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... 
bow_df=df

bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation'])

array_input=bow_df.Cleaned_text.values

bow_ds=df

vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000)

tf=vectoriser.fit(array_input)

vocab_countVectoriser=(tf.vocabulary_)

 vocab_countVectoriser.items()

dict_items([('cyclin', 1076), ('dependent', 1159), ('kinases', 2421), ('regulate', 3825), ('variety', 4877), ('cellular', 708), ('processes', 3572), ('cdk', 644), ('one', 3189), ('last', 2471), ('activating', 74), ('identified', 2146), ('kinase', 2409), ('activity', 91), ('revealed', 3955), ('previous', 3534), ('work', 4968), ('shown', 4146), ('silencing', 4185), ('increases', 2232), ('et', 1515), ('ets', 1519), ('virus', 4906), ('oncogene', 3182), ('homolog', 2094), ('activation', 77), ('mapk', 2663), ('pathway', 3286), ('confers', 916), ('resistance', 3906), ('breast', 535), ('cancer', 563), ('cells', 676), ('precise', 3501), ('mechanisms', 2721), ('generally', 1894), ('functions', 1838), ('remain', 3848), ('demonstrate', 1147), ('identifying', 2151), ('product', 3576), ('whose', 4951), ('mutations', 2953), ('cause', 631), ('syndrome', 4482), ('human', 2117), ('developmental', 1207), ('features', 1670), ('include', 2221), ('renal', 3859), ('show', 4142), ('associated', 339), ('mutants', 2927), ('unable', 4788), ('interact', 2317), ('increasing', 2233), ('raf', 3710), ('conferring', 915), ('phosphorylates', 3385), ('vitro', 4909), ('positively', 3474), ('controls', 985), ('degradation', 1133), ('proteasome', 3629), ('protein', 3630), ('levels', 2509), ('increased', 2229), ('derived', 1172), ('patient', 3289), ('increase', 2228), ('decreased', 1120), ('results', 3938), ('reveal', 3954), ('additional', 115), ('regulatory', 3832), ('mechanism', 2720), ('plays', 3424), ('key', 2404), ('roles', 3989), ('development', 1206), ('light', 2525), ('molecular', 2857), ('underlying', 4802), ('play', 3421), ('role', 3988), ('control', 981), ('number', 3148), ('genome', 1911), ('contains', 969), ('genes', 1901), ('encoding', 1445), ('proteins', 3644), ('considered', 941), ('members', 2753), ('family', 1656), ('owing', 3235), ('sequence', 4098), ('similarity', 4191), ('known', 2442), ('activated', 71), ('although', 214), ('discovered', 1249), ('almost', 200), ('ago', 154), ('remains', 3851), ('two', 4745), ('without', 4963), ('partner', 3273), ('knowledge', 2441), ('gap', 1856), ('largely', 2467), ('biological', 458), ('act', 65), ('positive', 3470), ('cell', 650), ('cycle', 1072), ('regulator', 3830), ('tumor', 4708), ('suppressor', 4461), ('others', 3219), ('interacts', 2321), ('transcription', 4626), ('factor', 1645), ('inhibits', 2279), ('transcriptional', 4631), ('unknown', 4818), ('knockdown', 2439), ('expression', 1612), ('activates', 73), ('induces', 2258), ('mcf', 2702), ('binding', 439), ('gene', 1884), ('predict', 3504), ('absence', 22), ('truncation', 4698), ('heterozygous', 2053), ('females', 1674), ('pathogenesis', 3279), ('recombinant', 3789), ('heterodimer', 2046), ('active', 85), ('phospho', 3377), ('erk', 1493), ('inducing', 2260), ('estrogen', 1514), ('receptor', 3780), ('er', 1485), ('phosphorylation', 3386), ('finally', 1730), ('detect', 1192), ('level', 2508), ('observed', 3161), ('yeast', 4989), ('hybrid', 2124), ('screen', 4048), ('interaction', 2319), ('signal', 4160), ('mouse', 2879), ('terminal', 4533), ('half', 1999), ('strong', 4373), ('homology', 2097), ('proposed', 3623), ('thus', 4581), ('performed', 3344), ('assays', 327), ('determine', 1198), ('whether', 4945), ('fig', 1692), ('isoform', 2368), ('expressed', 1606), ('produced', 3575), ('phenotype', 3368), ('full', 1815), ('length', 2492), ('detectable', 1193), ('partners', 3274), ('signals', 4168), ('albeit', 174), ('notably', 3113), ('weaker', 4930), ('respectively', 3918), ('tested', 4544), ('different', 1224), ('isoforms', 2369), ('alternative', 210), ('splicing', 4292), ('truncated', 4695), ('corresponding', 1008), ('products', 3578), ('mutated', 2931), ('found', 1787), ('patients', 3292), ('none', 3099), ('shorter', 4141), ('phenotypes', 3369), ('new', 3070), ('window', 4959), ('download', 1326), ('form', 1776), ('complex', 882), ('schematic', 4037), ('representation', 3879), ('analyzed', 245), ('amino', 219), ('acid', 50), ('numbers', 3152), ('indicated', 2246), ('black', 466), ('boxes', 496), ('indicate', 2245), ('internal', 2331), ('deletions', 1146), ('red', 3802), ('box', 495), ('indicates', 2247), ('compared', 866), ('assay', 321), ('set', 4120), ('fusion', 1841), ('dna', 1288), ('domain', 1306), ('interacting', 2318), ('activator', 83), ('empty', 1439), ('plasmids', 3413), ('expressing', 1607), ('lacz', 2457), ('used', 4840), ('reporter', 3874), ('blue', 481), ('region', 3822), ('assayed', 326), ('western', 4940), ('blot', 477), ('analysis', 238), ('myc', 3012), ('wt', 4973), ('kd', 2398), ('transfected', 4641), ('hek', 2025), ('immunoprecipitates', 2194), ('obtained', 3163), ('using', 4844), ('anti', 261), ('antibody', 269), ('correspond', 1007), ('total', 4611), ('lysates', 2619), ('goat', 1959), ('input', 2291), ('corresponds', 1009), ('lower', 2595), ('band', 394), ('upper', 4830), ('panel', 3254), ('endogenous', 1448), ('nonspecific', 3103), ('demonstrated', 1148), ('either', 1416), ('overexpression', 3231), ('seen', 4074), ('left', 2489), ('lane', 2459), ('another', 260), ('experiment', 1590), ('longer', 2580), ('gel', 1878), ('migration', 2791), ('examined', 1550), ('ability', 11), ('wild', 4955), ('type', 4755), ('dead', 1115), ('mutant', 2910), ('bearing', 420), ('substitution', 4409), ('atp', 355), ('embryonic', 1431), ('kidney', 2408), ('line', 2537), ('significantly', 4177), ('upon', 4829), ('coexpression', 830), ('lesser', 2499), ('extent', 1628), ('immunoprecipitated', 2193), ('detected', 1194), ('presence', 3520), ('coexpressed', 829), ('pair', 3245), ('confirmed', 920), ('observations', 3158), ('experiments', 1594), ('lack', 2453), ('robust', 3985), ('antibodies', 267), ('blotting', 480), ('readily', 3759), ('confirm', 918), ('physical', 3392), ('hypothesized', 2134), ('gst', 1985), ('combination', 855), ('confirming', 921), ('yet', 4993), ('model', 2841), ('purified', 3687), ('histone', 2082), ('substrate', 4411), ('alone', 201), ('next', 3075), ('investigated', 2353), ('whereas', 4944), ('glutathione', 1949), ('sepharose', 4096), ('matrix', 2687), ('capture', 593), ('blots', 479), ('top', 4610), ('middle', 2789), ('determined', 1200), ('added', 112), ('amounts', 228), ('visualized', 4908), ('staining', 4323), ('bottom', 491), ('activate', 69), ('involved', 2357), ('highly', 2073), ('specific', 4274), ('sirna', 4201), ('pools', 3459), ('mix', 2818), ('four', 1791), ('sirnas', 4202), ('low', 2591), ('final', 1729), ('concentration', 898), ('nm', 3092), ('targets', 4519), ('interestingly', 2324), ('pool', 3457), ('caused', 632), ('marked', 2671), ('decrease', 1119), ('suggest', 4423), ('stabilizes', 4312), ('induced', 2256), ('mrna', 2885), ('phosphorylated', 3383), ('similarly', 4192), ('expected', 1587), ('effects', 1393), ('sensitivity', 4090), ('similar', 4188), ('combined', 857), ('result', 3935), ('higher', 2067), ('drug', 1351), ('functional', 1823), ('negatively', 3048), ('regulates', 3827), ('response', 3923), ('mediated', 2728), ('quantification', 3697), ('quantitative', 3699), ('rt', 3999), ('pcr', 3311), ('explore', 1600), ('short', 4140), ('degraded', 1134), ('hypothesis', 2133), ('impact', 2197), ('flag', 1746), ('well', 4936), ('tagged', 4506), ('dramatically', 1343), ('excess', 1556), ('major', 2638), ('involves', 2359), ('treatment', 4678), ('inhibitor', 2276), ('mg', 2776), ('rescued', 3895), ('stability', 4308), ('cotransfected', 1014), ('vectors', 4887), ('latter', 2476), ('treated', 4675), ('proper', 3618), ('verified', 4892), ('mass', 2676), ('spectrometry', 4282), ('existence', 1567), ('multiple', 2902), ('residues', 3904), ('among', 225), ('positions', 3469), ('may', 2692), ('figs', 1708), ('finding', 1732), ('alanine', 173), ('substitutions', 4410), ('sites', 4206), ('small', 4229), ('significant', 4173), ('ser', 4108), ('establish', 1509), ('direct', 1244), ('link', 2543), ('differ', 1220), ('deletion', 1144), ('previously', 3538), ('apc', 275), ('cdh', 643), ('remained', 3849), ('insensitive', 2293), ('directly', 1248), ('studied', 4387), ('mutation', 2933), ('predicted', 3505), ('aberrant', 7), ('accordance', 39), ('incomplete', 2226), ('chromosome', 754), ('inactivation', 2216), ('showed', 4143), ('indeed', 2237), ('healthy', 2021), ('individual', 2252), ('vector', 4885), ('genetic', 1904), ('disease', 1253), ('far', 1660), ('within', 4962), ('fact', 1644), ('suggests', 4426), ('important', 2206), ('regulating', 3828), ('relevance', 3845), ('supported', 4451), ('seem', 4072), ('enhance', 1457), ('much', 2898), ('reduced', 3804), ('subject', 4398), ('ubiquitin', 4782), ('least', 2482), ('enhanced', 1458), ('independently', 2243), ('demonstrating', 1150), ('basis', 408), ('produce', 3574), ('hence', 2039), ('regardless', 3819), ('undergo', 4799), ('nonsense', 3101), ('suggested', 4424), ('give', 1937), ('rise', 3973), ('affected', 135), ('must', 2906), ('exhibit', 1563), ('compromised', 895), ('tissues', 4593), ('stages', 4321), ('seems', 4073), ('partially', 3269), ('achieved', 49), ('findings', 1733), ('offer', 3175), ('explanation', 1599), ('already', 204), ('reported', 3872), ('regulation', 3829), ('evidence', 1533), ('proline', 3603), ('called', 562), ('phosphorylate', 3382), ('motifs', 2876), ('non', 3097), ('directed', 1246), ('fashion', 1661), ('especially', 1505), ('context', 971), ('docking', 1302), ('high', 2058), ('affinity', 139), ('allow', 196), ('atypical', 363), ('including', 2224), ('cullin', 1060), ('ring', 3971), ('ligase', 2521), ('crl', 1042), ('identification', 2145), ('action', 68), ('ligases', 2523), ('will', 4958), ('require', 3886), ('studies', 4388), ('present', 3522), ('medical', 2732), ('implications', 2204), ('first', 1737), ('frequently', 1810), ('deregulated', 1170), ('many', 2660), ('cancers', 588), ('second', 4057), ('contribute', 976), ('understanding', 4804), ('causing', 634), ('predictive', 3509), ('clinical', 784), ('marker', 2673), ('hormone', 2100), ('therapy', 4561), ('third', 4565), ('interesting', 2323), ('transgenic', 4658), ('mice', 2782), ('showing', 4145), ('less', 2497), ('severe', 4128), ('abnormalities', 17), ('part', 3266), ('consequence', 933), ('risk', 3974), ('develop', 1203), ('certain', 717), ('types', 4771), ('various', 4878), ('models', 2844), ('three', 4570), ('copies', 993), ('exist', 1566), ('dosage', 1318), ('promote', 3608), ('growth', 1977), ('diagnosed', 1213), ('facilitate', 1641), ('general', 1893), ('particular', 3271), ('division', 1283), ('suppressive', 4460), ('exhibited', 1564), ('strongly', 4375), ('proliferation', 3598), ('cdnas', 649), ('plasmid', 3411), ('production', 3577), ('detailed', 1190), ('si', 4153), ('materials', 2683), ('methods', 2772), ('described', 1177), ('cultures', 1063), ('transfections', 4646), ('grew', 1970), ('dmem', 1285), ('supplemented', 4448), ('vol', 4913), ('fbs', 1665), ('invitrogen', 2355), ('rpmi', 3996), ('lipofectamine', 2551), ('combinations', 856), ('according', 40), ('instructions', 2307), ('transfection', 4644), ('fisher', 1740), ('scientific', 4042), ('collected', 840), ('pbs', 3305), ('centrifugation', 712), ('lysed', 2620), ('lysis', 2622), ('buffer', 546), ('containing', 967), ('mm', 2826), ('ph', 3359), ('mgcl', 2779), ('na', 3022), ('protease', 3627), ('mixture', 2820), ('roche', 3986), ('min', 2797), ('content', 970), ('immunoprecipitation', 2195), ('agarose', 143), ('beads', 419), ('incubated', 2235), ('washed', 4923), ('times', 4589), ('tris', 4687), ('nacl', 3024), ('edta', 1385), ('sample', 4012), ('heat', 2023), ('denatured', 1152), ('samples', 4014), ('sds', 4053), ('page', 3242), ('transferred', 4648), ('onto', 3198), ('nitrocellulose', 3088), ('membranes', 2755), ('processed', 3571), ('standard', 4324), ('procedures', 3569), ('following', 1774), ('primary', 3545), ('ab', 3), ('tubulin', 4707), ('santa', 4019), ('cruz', 1045), ('biotechnology', 464), ('made', 2627), ('dilution', 1237), ('sigma', 4158), ('actin', 66), ('hrp', 2115), ('coupled', 1019), ('sc', 4028), ('bio', 453), ('rad', 3708), ('rabbit', 3705), ('secondary', 4059), ('thermo', 4564), ('purification', 3686), ('generated', 1896), ('coli', 837), ('sf', 4132), ('bac', 386), ('system', 4488), ('infected', 2262), ('infection', 2263), ('ml', 2822), ('resuspended', 3944), ('lysate', 2618), ('soluble', 4254), ('fraction', 1796), ('time', 4586), ('see', 4068), ('glycerol', 1951), ('transformed', 4652), ('de', 1113), ('dtt', 1358), ('bar', 398), ('incubation', 2236), ('eluted', 1429), ('mixed', 2819), ('harboring', 2005), ('bsa', 543), ('ci', 757), ('hcl', 2009), ('volume', 4914), ('reactions', 3757), ('cut', 1069), ('stained', 4322), ('unrelated', 4825), ('normal', 3107), ('mother', 2874), ('table', 4496), ('supplementary', 4441), ('note', 3117), ('online', 3194), ('authors', 367), ('noted', 3118), ('overlap', 3232), ('represented', 3881), ('separate', 4092), ('autosomal', 372), ('dominant', 1314), ('mim', 2795), ('define', 1129), ('characteristic', 731), ('appearance', 281), ('apparent', 278), ('broad', 542), ('variable', 4864), ('figure', 1709), ('characterization', 733), ('cases', 612), ('illustrate', 2168), ('parental', 3261), ('consent', 932), ('ray', 3748), ('case', 611), ('right', 3968), ('compare', 865), ('array', 307), ('cgh', 722), ('data', 1094), ('log', 2574), ('ratio', 3745), ('represents', 3883), ('copy', 994), ('loss', 2586), ('six', 4209), ('probes', 3566), ('spanning', 4272), ('kb', 2397), ('probe', 3564), ('remove', 3857), ('structure', 4381), ('position', 3468), ('five', 1742), ('coding', 824), ('exons', 1583), ('green', 1969), ('encoded', 1443), ('arrow', 310), ('includes', 2223), ('exon', 1574), ('bars', 400), ('qpcr', 3694), ('sequencing', 4105), ('amplicon', 229), ('stop', 4357), ('codon', 827), ('splice', 4288), ('deleterious', 1139), ('alter', 205), ('conserved', 938), ('donor', 1317), ('acceptor', 32), ('site', 4203), ('intron', 2346), ('size', 4210), ('image', 2170), ('phenotypic', 3370), ('syndromes', 4483), ('ref', 3808), ('mycn', 3014), ('carried', 605), ('wide', 4953), ('resolution', 3914), ('oligonucleotide', 3180), ('comparative', 864), ('genomic', 1916), ('hybridization', 2125), ('severely', 4129), ('removed', 3858), ('real', 3764), ('excluded', 1559), ('unaffected', 4790), ('parents', 3263), ('enriched', 1462), ('followed', 1773), ('breakpoint', 532), ('cloning', 803), ('defined', 1130), ('exact', 1546), ('bp', 497), ('regions', 3823), ('entire', 1466), ('id', 2142), ('proven', 3653), ('routine', 3993), ('find', 1731), ('overlapping', 3233), ('available', 375), ('variation', 4874), ('databases', 1104), ('subsequently', 4403), ('individuals', 2253), ('literature', 2555), ('novo', 3121), ('point', 3440), ('remaining', 3850), ('affecting', 137), ('frameshift', 1803), ('immediately', 2181), ('premature', 3517), ('validated', 4856), ('independent', 2240), ('status', 4340), ('sporadic', 4296), ('female', 1673), ('larger', 2468), ('involving', 2360), ('analyzing', 249), ('complete', 879), ('suggesting', 4425), ('carrying', 609), 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featurenamesforbow=vectoriser.get_feature_names()

featurenamesforbow

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 ...]
array_transformed=vectoriser.transform(array_input)

array_transformedone=array_transformed.toarray()

dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow )

dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1)

dataframofBowForText.head()
aa aacrjournals aacrjournals org ab abbreviations abc abd aberrant aberrant splicing aberrations abi ability ability bind ability induce abl able abnormal abnormalities abolish abolished abrogate abrogated absence absent absolute abstract abundance abundant ac acc accelerated accepted acceptor access access image accessible accessible alternative accession accompanied accordance ... withdrawal within without wm wm cells wnt women work worldwide worse wpd written wt wt mutant wt wt wtb wtb raf wu xenograft xenografts xenopus xl xp xrcc xu yap year years yeast yeast cells yellow yes yet yield yielded young zhang zinc zn Class
0 0 0 0 1 0 0 0 2 1 0 0 1 0 0 0 0 0 2 0 0 0 0 3 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 ... 0 3 3 0 0 0 0 2 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2 0 0 0 0 0 0 1
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5 rows × 5001 columns

dataframofBowForText.to_csv("datafraneofBowTextFeature.csv")

2.TFIDF featurisation of text data

</div> </div> </div> 
df.head()
ID Gene Variation Class Cleaned_text
0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 CBL W802* 2 abstract background non small cell lung cancer...
2 2 CBL Q249E 2 abstract background non small cell lung cancer...
3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... 
tfidf_df=df
tfidf_ds=df

tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class'])

tfidf_df
Cleaned_text
0 cyclin dependent kinases cdks regulate variety...
1 abstract background non small cell lung cancer...
2 abstract background non small cell lung cancer...
3 recent evidence demonstrated acquired uniparen...
4 oncogenic mutations monomeric casitas b lineag...
... ...
3316 introduction myelodysplastic syndromes mds het...
3317 introduction myelodysplastic syndromes mds het...
3318 runt related transcription factor gene runx al...
3319 runx gene frequent target chromosomal transloc...
3320 frequent mutations associated leukemia recurre...

3321 rows × 1 columns

array_input_tfidf=tfidf_df['Cleaned_text'].values

vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000)

vectorizer.fit(array_input_tfidf)

TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.float64'>, encoding='utf-8',
                input='content', lowercase=True, max_df=1.0, max_features=1000,
                min_df=3, ngram_range=(1, 2), norm='l2', preprocessor=None,
                smooth_idf=True, stop_words=None, strip_accents=None,
                sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None, use_idf=True, vocabulary=None)
len(vectorizer.vocabulary_)

1000
featurenames_tfidf=vectorizer.get_feature_names()

tfidf_vocab=vectorizer.vocabulary_

transformed_tfidf_csr=vectorizer.transform(array_input_tfidf)

transformed_tfidf_csr=transformed_tfidf_csr.toarray()

arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf)

tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1)

tfidf_textfeature.to_csv("tfidf_textfeatures.csv")

tfidf_textfeature
aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional adenocarcinoma advanced affect affected affecting affinity age akt al ala alk allele alleles alone alterations altered alternative although ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 0.000000 0.0 0.000000 0.007996 0.003394 0.003430 0.000000 0.000000 0.000000 0.0 0.0 0.005836 0.000000 0.017823 0.000000 0.000000 0.009851 0.003929 ... 0.000000 0.011351 0.011977 0.000000 0.009670 0.024498 0.000000 0.000000 0.000000 0.000000 0.002678 0.007361 0.000000 0.000000 0.0 0.000000 0.000000 0.015961 0.000000 0.003635 0.0 0.0 0.014516 0.000000 0.005902 0.047440 0.048425 0.004343 0.010854 0.000000 0.006067 0.006067 0.004734 0.006265 0.007224 0.028735 0.000000 0.017848 0.005846 1
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2 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 0.014053 0.0 0.007465 0.002027 0.002582 0.000000 0.002612 0.002856 0.001877 0.0 0.0 0.006657 0.002446 0.006778 0.016340 0.000000 0.000000 0.004482 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2
3 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 0.000000 0.0 0.000000 0.026962 0.007630 0.000000 0.003861 0.000000 0.000000 0.0 0.0 0.009838 0.018076 0.003339 0.000000 0.000000 0.000000 0.017664 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3
4 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 0.000000 0.0 0.010418 0.009430 0.007205 0.031548 0.000000 0.000000 0.000000 0.0 0.0 0.008259 0.000000 0.000000 0.000000 0.000000 0.004647 0.006950 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 0.000000 0.0 0.001547 0.000000 0.000000 0.000000 0.004330 0.000000 0.006224 0.0 0.0 0.000000 0.000000 0.013106 0.003385 0.000000 0.000000 0.004953 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4
3317 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 0.000000 0.0 0.001989 0.000000 0.000000 0.000000 0.005570 0.000000 0.008005 0.0 0.0 0.000000 0.000000 0.016859 0.002177 0.000000 0.000000 0.006371 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1
3318 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.003138 0.000000 0.000000 0.005647 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.001402 0.001502 0.002697 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1
3319 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 0.000000 0.0 0.000000 0.018975 0.000000 0.002713 0.005434 0.000000 0.093724 0.0 0.0 0.013848 0.005089 0.011748 0.025491 0.000000 0.023376 0.004662 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4
3320 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 0.000000 0.0 0.003222 0.025663 0.000000 0.009006 0.003006 0.000000 0.055096 0.0 0.0 0.010216 0.004223 0.010400 0.015279 0.000000 0.014371 0.005158 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4

3321 rows × 1001 columns

Q3. Is this Bow,tfidf for text feature Useful in our classification?

</div> </div> </div>

Lets find out by tsne and one simple model.

  • TSNE for BOW TEXT Feature
x_std=dataframofBowForText.iloc[:, 0:5000].values

y_true_std=dataframofBowForText.Class.values

stdnd=StandardScaler()
data=stdnd.fit_transform(x_std)

intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"])  , df["Class"]],axis=1)

intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"])  , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df10,hue='Class',height=5)\
    .map(plt.scatter , 'Dim1' , 'Dim2')\
    .add_legend()
plt.show()

we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well

Tsne for TFIDF data

x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values

y_tfidf_true=tfidf_textfeature.Class.values

stdnd=StandardScaler()
data=stdnd.fit_transform(x_tfidf_true)

intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"])  , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df12,hue='Class',height=5)\
    .map(plt.scatter , 'Dim1' , 'Dim2')\
    .add_legend()
plt.show()

we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well

Lets do analisys based on models for the two featurisations and compare the results

Lets go with simple model like logistic regression for high dimension data

  • 1.BOW Feature for text
def oneHot_Logsitic(var1,var2):
  """
  This function is use to build n hyperparamater tune SVM
  """
  X = var1
  y_true = var2

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print ( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  #Svm HyperTuning
  logLoss_train=[ ]
  logLoss_cv=[ ]
  c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
  for i in c:
        clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
        clf_SGD.fit(x_train,y_train)
        
        calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
        calibrated.fit(x_train , y_train)
        
        predict_y = calibrated.predict_proba(x_train)
        predict_y2 = calibrated.predict_proba(x_cv)
        
        logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
        
        logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
        
        

        

        

        
    #plt.plot((c), train_auc, label='Train AUC')
    #plt.plot((c), cv_auc, label='CV AUC')

  plt.plot(np.log(c), logLoss_train, label='Train logloss')
  plt.plot(np.log(c), logLoss_cv, label='CV logloss')

  plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
  plt.legend()

  plt.xlabel("C: hyperparameter")
  plt.ylabel("LogLoss")
  plt.title("ERROR PLOTS")
  return(plt.show())



  

oneHot_Logsitic( data,y_true_std)

(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)

this one was with standardised data

oneHot_Logsitic( x_std,y_true_std)

(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)

this one is without standardised data

from the graphs i can take my alpha as 0.001

Testing on test datasets

def logistic_test(var1,var2):
  """
  This function is used to create model and tune it to find best hyper parameter
  """
  x_true=var1
  y_true=var2


  x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)



  
  print("The shape of the train n test vector as follows:")
  print(x_train.shape,y_train.shape)
  print(x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  log_loss_train=[]
  log_loss_cv=[]
  log_loss_test=[]

  
  model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
  model.fit(x_train , y_train)
      
  
  clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
  clf.fit(x_train,y_train)

  pred_ytrain=clf.predict_proba(x_train)
  log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))

  pred_ycv=clf.predict_proba(x_cv)
  log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))

  pred_ytest=clf.predict_proba(x_test)
  log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))



  print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train ))
  print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv ))
  print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test ))

logistic_test(x_std,y_true_std)

The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [1.0724656346673704]
The Logloss for 0.001 the coresponding cv loss is [1.2723948073040368]
The Logloss for 0.001 the coresponding test loss is [1.3034431218014317]

Above is result on normal data

logistic_test(data,y_true_std)

The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [0.9441582057917238]
The Logloss for 0.001 the coresponding cv loss is [1.210222073584811]
The Logloss for 0.001 the coresponding test loss is [1.1539667998430334]

Above is Result on standardised data

  • we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective Lets look at other featurisations as well.
  • 2.TFIDF feauture for text

Using Logisticmodels we bult for previous cases

Tuning

oneHot_Logsitic(x_tfidf_true,y_tfidf_true)

(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)

this one was without standardised data

oneHot_Logsitic(data,y_tfidf_true)

(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)

this one was with standardised data

From the graphs i can select my alpha as 0.001

Lets Test the model

without standardised data

logistic_test(x_tfidf_true,y_tfidf_true)

The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [1.0655639055266641]
The Logloss for 0.001 the coresponding cv loss is [1.287554414069922]
The Logloss for 0.001 the coresponding test loss is [1.2673057224719144]

with standardised data

logistic_test(data,y_tfidf_true)

The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [0.9062325983187661]
The Logloss for 0.001 the coresponding cv loss is [1.154476481391309]
The Logloss for 0.001 the coresponding test loss is [1.2146295572433117]

Observations of Text featureised models with Standardised data only

</p> </div> </div> </div> 
table=PrettyTable()

table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]

table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153])
table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214])
print(table)

+--------------+--------------------+----------+----------------+----------------+-------------+--------------+
| Feature Name | Featurisation Used |  Model   | HyperParameter |  train Logloss |  Cv Logloss | test Logloss |
+--------------+--------------------+----------+----------------+----------------+-------------+--------------+
|     Text     |       TFIDF        | Logistic |     0.001      |     0.944      |     1.21    |    1.153     |
|     Text     |        BOW         | Logistic |     0.001      |     0.906      |    1.544    |    1.214     |
+--------------+--------------------+----------+----------------+----------------+-------------+--------------+

TFIDF Featurisation is more sensible comparitively than BOW featurisation

Q4.Are these Features stable accross all data sets?

</div> </div> </div>

Yes,because there is no much difference in cv n test log loss.

Now we have all the vectors lets go for modelling

</div> </div> </div>

4.0 Modelling

</p> </div> </div> </div>

4.1 Creating Miscellenous Functions

Confusion Matrix,Precision,Recall

</p> </div> </div> </div> 
def plot_confusion_matrix(test_y, predict_y):
    C = confusion_matrix(test_y, predict_y)
    # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
    
    A =(((C.T)/(C.sum(axis=1))).T)
    #divid each element of the confusion matrix with the sum of elements in that column
    
    # C = [[1, 2],
    #     [3, 4]]
    # C.T = [[1, 3],
    #        [2, 4]]
    # C.sum(axis = 1)  axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
    # C.sum(axix =1) = [[3, 7]]
    # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
    #                           [2/3, 4/7]]

    # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
    #                           [3/7, 4/7]]
    # sum of row elements = 1
    
    B =(C/C.sum(axis=0))
    #divid each element of the confusion matrix with the sum of elements in that row
    # C = [[1, 2],
    #     [3, 4]]
    # C.sum(axis = 0)  axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
    # C.sum(axix =0) = [[4, 6]]
    # (C/C.sum(axis=0)) = [[1/4, 2/6],
    #                      [3/4, 4/6]] 
    
    labels = [1,2,3,4,5,6,7,8,9]
    # representing A in heatmap format
    print("-"*20, "Confusion matrix", "-"*20)
    plt.figure(figsize=(20,7))
    sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
    plt.xlabel('Predicted Class')
    plt.ylabel('Original Class')
    plt.show()

   
    
    # representing B in heatmap format
    print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
    plt.figure(figsize=(20,7))
    sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
    plt.xlabel('Predicted Class')
    plt.ylabel('Original Class')
    plt.show()

    print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
    plt.figure(figsize=(20,7))
    sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
    plt.xlabel('Predicted Class')
    plt.ylabel('Original Class')
    plt.show()

Loading the df and manupulating

#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")

#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")


#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")

#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")

variation_df_featureHashing.columns

Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object')
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])

variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])

text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])

df=df.drop(columns=['Unnamed: 0'])

gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns

Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid',
       'acids', 'acquired', 'across',
       ...
       'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt',
       'years', 'yeast', 'yet'],
      dtype='object', length=1000)
#1.(gene meanResponse + variation meanResponse +  text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response 
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors  
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.

1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset 

gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)

withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)

gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns

strndzn=StandardScaler()

std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)

std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)

std_gene_var_text_meanTdidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")

2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised 

gene_vector_oneHot,variation_df_oneHot,text_df_tfidf

clmns=text_df_tfidf.columns

text_stdr=strndzn.fit_transform(text_df_tfidf)

df1=pd.DataFrame(text_stdr,columns=clmns)

std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)

without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)

std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')

std_gene_var_text_onehottifidf
ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 4261 columns

3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version 

gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf

std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)

std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')

std_gene_var_text_hashingtfidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

Now we have our data sets lets perform modelling.

Datasets info:-

  • gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)

  • gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)

  • gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.

Lets Create Models

4.1 NaiveBayes Model

</p> </div> </div> </div>

DatasetsUsedHere:-

  • gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)

  • gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)

  • gene(hashed) + variation(hashed) + text(tfidf-Bigrams)

A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) 
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values

from collections import Counter
Counter(varB_NB)

Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37})
def naiveBayesTunning(var1,var2):
  """
  This function is used to tune naiveBayes Model on data and find right Hyper parameter
  """

  X=var1
  Y=var2

  #Splitting the data

  x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)

  #hyperTuning
  train_logloss = []
  cv_logloss = []
  test_logloss = []
  Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
  for i in Alpha:

    MNB = MultinomialNB(alpha=i)
    MNB.fit(x_train, y_train)
    
    sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
    sig_clf.fit(x_train,y_train)

    y_train_pred =  sig_clf.predict_proba(x_train)
    y_cv_pred =  sig_clf.predict_proba(x_cv)
    #y_test_pred = sig_clf.predict_proba(x_test)
   
    train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
    cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
    #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
    
    
  plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
  plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
  plt.legend()
  plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
  plt.xlabel("log(alpha)")
  plt.ylabel("logloss")
  plt.title("ERROR PLOTS")
  return(plt.show())

naiveBayesTunning(varA_NB,varB_NB)

From the above graph i can take my alpha value as 1.

def naiveBayesTesting_(var1,var2):
  """
  This function is used to tune naiveBayes Model on data and find right Hyper parameter
  """

  X=var1
  Y=var2

  #Splitting the data

  x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)

  #hyperTuning
  train_logloss = []
  cv_logloss = []
  test_logloss = []
  
  MNB = MultinomialNB(alpha=1)
  MNB.fit(x_train, y_train)
  
  sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
  sig_clf.fit(x_train,y_train)

  y_train_pred =  sig_clf.predict_proba(x_train)
  y_cv_pred =  sig_clf.predict_proba(x_cv)
  y_test_pred = sig_clf.predict_proba(x_test)

  train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
  cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
  test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))

  print("The  hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
  print("The  hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
  print("The  hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
  plot_confusion_matrix(y_test,  sig_clf.predict(x_test))

naiveBayesTesting_(varA_NB,varB_NB)

The  hyper parameter and logloss for Train data are :1 and [0.9633295236937504]
The  hyper parameter and logloss for Train data are :1 and [1.1924015413394058]
The  hyper parameter and logloss for Train data are :1 and [1.1982778811800479]
-------------------- Confusion matrix --------------------

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:

invalid value encountered in true_divide


-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------
  • This is the Base Line Model Results it performed well.
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) 
withoutstd_gene_var_text_meanTdidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 ... 0.000000 0.011351 0.011977 0.000000 0.009670 0.024498 0.000000 0.000000 0.000000 0.000000 0.002678 0.007361 0.000000 0.000000 0.0 0.000000 0.000000 0.015961 0.000000 0.003635 0.0 0.0 0.014516 0.000000 0.005902 0.047440 0.048425 0.004343 0.010854 0.000000 0.006067 0.006067 0.004734 0.006265 0.007224 0.028735 0.000000 0.017848 0.005846 1
1 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2
2 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2
3 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3
4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4
3317 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1
3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1
3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4
3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4

3321 rows × 1019 columns

varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values

varB_NB_=without_std_gene_var_text_onehottifidf.Class.values

Reusing the above functions

naiveBayesTunning(varA_NB_ , varB_NB_)

from above graph i can select alpha as 1

naiveBayesTesting_(varA_NB_ , varB_NB_)

The  hyper parameter and logloss for Train data are :1 and [1.0383832549539975]
The  hyper parameter and logloss for Train data are :1 and [1.2612556656065605]
The  hyper parameter and logloss for Train data are :1 and [1.2320081611414992]
-------------------- Confusion matrix --------------------

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:

invalid value encountered in true_divide


-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

4.2 Knn Model

</p>

DatasetsUsedHere:-

  • gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)

  • gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)

  • gene(hashed) + variation(hashed) + text(tfidf-Bigrams)

</div> </div> </div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) 
dash_dfff=std_gene_var_text_onehottifidf

varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values

from collections import Counter
Counter(varB_knn)

Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37})

Balancing the data set is important in knn as knn doesnt have balancing option in model parameters

from imblearn.combine import SMOTETomek

smk=SMOTETomek(random_state=42)

x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)

Counter(y_res)

Counter({1: 917,
         2: 921,
         3: 945,
         4: 926,
         5: 932,
         6: 941,
         7: 913,
         8: 953,
         9: 953})
def knnTuning(var1, var2):
  """
  This Function is used to perform KNNtuning and decide the K hyper parameter
  """
  X=var1
  Y=var2

  #Splitting the data

  x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)

  #hyperTuning
  train_logloss = []
  cv_logloss = []
  test_logloss = []

  K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
  for i in K:
    neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
    neigh.fit(x_train, y_train)
    
    sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
    sig_clf.fit(x_train,y_train)

    y_train_pred =  sig_clf.predict_proba(x_train)
    y_cv_pred =  sig_clf.predict_proba(x_cv)
    #y_test_pred = sig_clf.predict_proba(x_test)
   
    train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
    cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
    #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
    
    
  plt.plot(K, train_logloss, label='Train Logloss')
  plt.plot(K, cv_logloss ,label='CV logloss')
  plt.legend()
  plt.scatter(K,cv_logloss , label='CV logloss')
  plt.xlabel("K")
  plt.ylabel("logloss")
  plt.title("ERROR PLOTS")
  return(plt.show())

knnTuning(x_res,y_res)

From above graph i can select my k value as 31

def knnTesting(var1, var2):
  """
  This Function is used to perform KNNtuning and decide the K hyper parameter
  """
  X=var1
  Y=var2

  #Splitting the data

  x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)

  #hyperTuning
  train_logloss = []
  cv_logloss = []
  test_logloss = []

  neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
  neigh.fit(x_train, y_train)  
  
  sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
  sig_clf.fit(x_train,y_train)

  y_train_pred =  sig_clf.predict_proba(x_train)
  y_cv_pred =  sig_clf.predict_proba(x_cv)
  y_test_pred = sig_clf.predict_proba(x_test)
  
  train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
  cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
  test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))

  print("The  hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
  print("The  hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
  print("The  hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
  plot_confusion_matrix(y_test,  sig_clf.predict(x_test))
    
    
  

knnTesting(x_res,y_res)

The  hyper parameter and logloss for Train data are :31 and [0.813976747977174]
The  hyper parameter and logloss for cv data are :31 and [0.8788933809062333]
The  hyper parameter and logloss for Test data are :31 and [0.8810438075247502]
-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) 

std_gene_var_text_meanTdidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
balancing class

using SMOTE

from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.


Counter({1: 921,
         2: 926,
         3: 949,
         4: 926,
         5: 932,
         6: 944,
         7: 919,
         8: 953,
         9: 953})

Tuning using the previous functions

knnTuning(x_res,y_res)

from the above graph i can take k =21

knnTesting(x_res,y_res)

The  hyper parameter and logloss for Train data are :31 and [0.6814415244209885]
The  hyper parameter and logloss for cv data are :31 and [0.7016829214824364]
The  hyper parameter and logloss for Test data are :31 and [0.7779077172643827]
-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) 

std_gene_var_text_hashingtfidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
Balancing using smote 
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.


Counter({1: 915,
         2: 916,
         3: 944,
         4: 923,
         5: 924,
         6: 938,
         7: 907,
         8: 953,
         9: 953})

tuning model and testing model with old functions created above

knnTuning(x_res,y_res)

From the graph i can take k as 15

knnTesting(x_res,y_res)

The  hyper parameter and logloss for Train data are :15 and [0.6474458212209986]
The  hyper parameter and logloss for cv data are :15 and [0.7512635680512498]
The  hyper parameter and logloss for Test data are :31 and [0.7368556387957963]
-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

4.3 Logistic Regression

</p> </div> </div> </div>

DatasetsUsedHere:-

  • gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)

  • gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)

  • gene(hashed) + variation(hashed) + text(tfidf-Bigrams)

A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) 
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values

def tune_Logsitic(var1,var2):
  """
  This function is use to build n hyperparamater tune logisticmodel
  """
  X = var1
  y_true = var2

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print ( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  # HyperTuning
  logLoss_train=[ ]
  logLoss_cv=[ ]
  c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
  for i in c:
        clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
        clf_SGD.fit(x_train,y_train)
        
        calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
        calibrated.fit(x_train , y_train)
        
        predict_y = calibrated.predict_proba(x_train)
        predict_y2 = calibrated.predict_proba(x_cv)
        
        logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
        
        logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
        
        

        

        

    

  plt.plot(np.log(c), logLoss_train, label='Train logloss')
  plt.plot(np.log(c), logLoss_cv, label='CV logloss')

  plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
  plt.legend()

  plt.xlabel("C: hyperparameter")
  plt.ylabel("LogLoss")
  plt.title("ERROR PLOTS")
  return(plt.show())



  

tune_Logsitic(varA_log,varB_knn)

(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)

From the graph i can take aplha as 1

def test_Logistic(var1,var2):
  """
  This function is used to test model on test data.
  """
  x_true=var1
  y_true=var2


  x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)



  
  print("The shape of the train n test vector as follows:")
  print(x_train.shape,y_train.shape)
  print(x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  log_loss_train=[]
  log_loss_cv=[]
  log_loss_test=[]

  
  model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
  model.fit(x_train , y_train)
  
      
  
  clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
  clf.fit(x_train,y_train)

  pred_ytrain=clf.predict_proba(x_train)
  log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))

  pred_ycv=clf.predict_proba(x_cv)
  log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))

  pred_ytest=clf.predict_proba(x_test)
  log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))



  print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
  print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
  print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
  plot_confusion_matrix(y_test,  clf.predict(x_test))

test_Logistic(varA_log,varB_log)

The shape of the train n test vector as follows:
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
The Logloss for 1 the coresponding train loss is [0.7850156568089277]
The Logloss for 1 the coresponding cv loss is [1.1961412442201744]
The Logloss for 1 the coresponding test loss is [1.1748189424714095]
-------------------- Confusion matrix --------------------

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:

invalid value encountered in true_divide


-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) 

std_gene_var_text_meanTdidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values

for tunning and testing using above functions

tune_Logsitic(vara_mean,varb_mean)

(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)

from the above graph i can conclude c=1

test_Logistic(vara_mean,varb_mean)

The shape of the train n test vector as follows:
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logloss for 1 the coresponding train loss is [0.5847329559769139]
The Logloss for 1 the coresponding cv loss is [0.8572068041315724]
The Logloss for 1 the coresponding test loss is [0.8214264881831812]
-------------------- Confusion matrix --------------------

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:

invalid value encountered in true_divide


-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) 

std_gene_var_text_hashingtfidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values

tune_Logsitic(vara_hashed,varb_hashed)

(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)

from graph i can take C as 1

test_Logistic(vara_hashed,varb_hashed)

The shape of the train n test vector as follows:
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logloss for 1 the coresponding train loss is [0.8735667598121121]
The Logloss for 1 the coresponding cv loss is [1.1928582990002885]
The Logloss for 1 the coresponding test loss is [1.1999154339757783]
-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

4.4 SVM

</p>

DatasetsUsedHere:-

  • gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)

  • gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)

  • gene(hashed) + variation(hashed) + text(tfidf-Bigrams)

</div> </div> </div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) 
std_gene_var_text_onehottifidf
ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 4261 columns

vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values

def tuneSVM(var1,var2):
  """
  This function is use to build n hyperparamater tune SVM
  """
  X = var1
  y_true = var2

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print ( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  #Svm HyperTuning
  logLoss_train=[ ]
  logLoss_cv=[ ]
  c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
  for i in c:
        clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
        clf_SGD.fit(x_train,y_train)
        
        calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
        calibrated.fit(x_train , y_train)
        
        predict_y = calibrated.predict_proba(x_train)
        predict_y2 = calibrated.predict_proba(x_cv)
        
        logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
        
        logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
        
        

        

        

   

  plt.plot(np.log(c), logLoss_train, label='Train logloss')
  plt.plot(np.log(c), logLoss_cv, label='CV logloss')

  plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
  plt.legend()

  plt.xlabel("C: hyperparameter")
  plt.ylabel("LogLoss")
  plt.title("ERROR PLOTS")
  return(plt.show())

tuneSVM(vara_svm,varb_svm)

(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)

from the graph i can select c = 0.1

def test_SVMModel(var1 , var2):

  X = var1
  y_true = var2

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)
  
  logLoss_test=[ ]
  logLoss_cv_new=[ ]
  logLoss_train_new=[ ]
  
  
  clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
  clf_SGD_test.fit(x_train,y_train)

  calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
  calibrated.fit(x_train , y_train)
  
  predict_y_test = calibrated.predict_proba(x_test)
  logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
  
  predicted_y_train=calibrated.predict_proba(x_train)
  logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))

  predicted_y_cv=calibrated.predict_proba(x_cv)
  logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
  

  print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
  print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))  
  print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
  plot_confusion_matrix(y_test,  calibrated.predict(x_test))

test_SVMModel(vara_svm,varb_svm)

(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] 
The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932]
The Logg loss for test data with best aplha 0.1 is [1.1689839246304874]
-------------------- Confusion matrix --------------------

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:

invalid value encountered in true_divide


-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) 

std_gene_var_text_meanTdidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values

using previous functions for model

tuneSVM(vara_mean_svm,varb_mean_svm)

(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)

from above c =1

test_SVMModel(vara_mean_svm,varb_mean_svm)

(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logg loss for training data with best aplha 1 is [0.8782130606666145] 
The Logg loss for cv data with best aplha 1 is [1.104589994061247]
The Logg loss for test data with best aplha 1 is [1.0574913033666131]
-------------------- Confusion matrix --------------------

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:

invalid value encountered in true_divide


-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) 

std_gene_var_text_hashingtfidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values

tuneSVM(vara_svm_hashed,varb_svm_hashed)

(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)

from above graph i can take c as 1

test_SVMModel(vara_svm_hashed,varb_hashed)

(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logg loss for training data with best aplha 1 is [0.9725367454691902] 
The Logg loss for cv data with best aplha 1 is [1.1331135752614927]
The Logg loss for test data with best aplha 1 is [1.2037472465904475]
-------------------- Confusion matrix --------------------

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:

invalid value encountered in true_divide


-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

4.5 Random Forest

</p>

DatasetsUsedHere:-

  • gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)

  • gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)

  • gene(hashed) + variation(hashed) + text(tfidf-Bigrams)

</div> </div> </div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) 
std_gene_var_text_onehottifidf
ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 4261 columns

vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values

def tune_randomforest(vara,varb):
  """
  This function is used to tune rf model
  """
  X = vara
  y_true = varb

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print ( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  # HyperTuning
  logLoss_train=[ ]
  logLoss_cv=[ ]
  logLoss_test=[ ]

  estimators = [100,200,500,1000,2000]
  max_depth = [3, 5, 7,10]
  
  for i in estimators:
      for j in max_depth:
          
          print("for n_estimators =", i,"and max depth = ", j)
          clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
          clf.fit(x_train, y_train)
          
          sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
          sig_clf.fit(x_train, y_train)
          
          sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
          sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)

          logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
          logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
          
          print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) 
          print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) 

tune_randomforest(vara_RF_onhot,varb_RF_onhot)

(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
for n_estimators = 100 and max depth =  3
Log Loss cv: 1.2606812233244562
Log Loss train: 1.1374431015135666
for n_estimators = 100 and max depth =  5
Log Loss cv: 1.1653337133462423
Log Loss train: 0.9371070459846149
for n_estimators = 100 and max depth =  7
Log Loss cv: 1.12209548509024
Log Loss train: 0.7363291411007856
for n_estimators = 100 and max depth =  10
Log Loss cv: 1.10268480641603
Log Loss train: 0.5519688514792611
for n_estimators = 200 and max depth =  3
Log Loss cv: 1.248631824458489
Log Loss train: 1.1184306206599595
for n_estimators = 200 and max depth =  5
Log Loss cv: 1.1564275473529837
Log Loss train: 0.919144190096995
for n_estimators = 200 and max depth =  7
Log Loss cv: 1.110794380130624
Log Loss train: 0.7207481407448507
for n_estimators = 200 and max depth =  10
Log Loss cv: 1.0961422234706304
Log Loss train: 0.5423272076360501
for n_estimators = 500 and max depth =  3
Log Loss cv: 1.2478678013942157
Log Loss train: 1.114370022235754
for n_estimators = 500 and max depth =  5
Log Loss cv: 1.148009893446
Log Loss train: 0.9050771353940641
for n_estimators = 500 and max depth =  7
Log Loss cv: 1.1026865237241201
Log Loss train: 0.7101699614090803
for n_estimators = 500 and max depth =  10
Log Loss cv: 1.0917480510399207
Log Loss train: 0.5373319633612905
for n_estimators = 1000 and max depth =  3
Log Loss cv: 1.2414453238075647
Log Loss train: 1.1053160288591364
for n_estimators = 1000 and max depth =  5
Log Loss cv: 1.1452379981760221
Log Loss train: 0.900926228080795
for n_estimators = 1000 and max depth =  7
Log Loss cv: 1.0990520521977565
Log Loss train: 0.7050443392267894
for n_estimators = 1000 and max depth =  10
Log Loss cv: 1.0908700848502797
Log Loss train: 0.5357777692154363
for n_estimators = 2000 and max depth =  3
Log Loss cv: 1.240682648359384
Log Loss train: 1.1036497793577018
for n_estimators = 2000 and max depth =  5
Log Loss cv: 1.1411916861993632
Log Loss train: 0.8950347894321122
for n_estimators = 2000 and max depth =  7
Log Loss cv: 1.0971090158549015
Log Loss train: 0.7018660431272183
for n_estimators = 2000 and max depth =  10
Log Loss cv: 1.0925882642141658
Log Loss train: 0.5355792048858152

from above data i can select esimators 2000 and depth=5

def testRF(vara,varb):
  """
  This function is used to test rf model
  """
  X = vara
  y_true = varb

  x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
  x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

  print ( x_train.shape,y_train.shape)
  print( x_cv.shape,y_cv.shape)
  print(x_test.shape,y_test.shape)

  # HyperTuning
  logLoss_train=[ ]
  logLoss_cv=[ ]
  logLoss_test=[ ]

  
          
 
  clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
  clf.fit(x_train, y_train)
  
  sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
  sig_clf.fit(x_train, y_train)
  
  sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
  sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
  sig_clf_probs_ytest = sig_clf.predict_proba(x_test)

  logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
  logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
  logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
  
  print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) 
  print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
  print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))

  plot_confusion_matrix(y_test,  sig_clf.predict(x_test))

testRF(vara_RF_onhot,varb_RF_onhot)

(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
Log Loss cv: 1.11768909971263
Log Loss train: 0.9002694349418067
Log Loss test: 1.1477524391499037

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:

invalid value encountered in true_divide


-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) 

std_gene_var_text_meanTdidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values

tune_randomforest(vara_mean_rf,varb_mean_rf)

(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
for n_estimators = 100 and max depth =  3
Log Loss cv: 0.23570046546599333
Log Loss train: 0.19363398591104833
for n_estimators = 100 and max depth =  5
Log Loss cv: 0.22955561081586118
Log Loss train: 0.16166542392105412
for n_estimators = 100 and max depth =  7
Log Loss cv: 0.23627364969006484
Log Loss train: 0.13622754279491436
for n_estimators = 100 and max depth =  10
Log Loss cv: 0.23299169854143326
Log Loss train: 0.09219438260645411
for n_estimators = 200 and max depth =  3
Log Loss cv: 0.15347165131328178
Log Loss train: 0.1289817833963096
for n_estimators = 200 and max depth =  5
Log Loss cv: 0.18867692478728829
Log Loss train: 0.1333800470656138
for n_estimators = 200 and max depth =  7
Log Loss cv: 0.19812746725522537
Log Loss train: 0.11363125813131297
for n_estimators = 200 and max depth =  10
Log Loss cv: 0.2073843284191654
Log Loss train: 0.08264026371353654
for n_estimators = 500 and max depth =  3
Log Loss cv: 0.14838448934316534
Log Loss train: 0.12796172100789224
for n_estimators = 500 and max depth =  5
Log Loss cv: 0.1732893968732289
Log Loss train: 0.1257528101073332
for n_estimators = 500 and max depth =  7
Log Loss cv: 0.18502788808193749
Log Loss train: 0.1059774981029388
for n_estimators = 500 and max depth =  10
Log Loss cv: 0.20316074184161176
Log Loss train: 0.0820099522892585
for n_estimators = 1000 and max depth =  3
Log Loss cv: 0.15096173695782245
Log Loss train: 0.13021826710995227
for n_estimators = 1000 and max depth =  5
Log Loss cv: 0.17583691544250576
Log Loss train: 0.12659716596839768
for n_estimators = 1000 and max depth =  7
Log Loss cv: 0.1862603932648018
Log Loss train: 0.10603952186939303
for n_estimators = 1000 and max depth =  10
Log Loss cv: 0.20453842968817895
Log Loss train: 0.08168228699376241
for n_estimators = 2000 and max depth =  3
Log Loss cv: 0.14749154103551826
Log Loss train: 0.1272450725093758
for n_estimators = 2000 and max depth =  5
Log Loss cv: 0.16918496782525078
Log Loss train: 0.12115201697127558
for n_estimators = 2000 and max depth =  7
Log Loss cv: 0.1820690821751895
Log Loss train: 0.10282294620866399
for n_estimators = 2000 and max depth =  10
Log Loss cv: 0.1984320940418906
Log Loss train: 0.07940530139460128

from the above i can take estimators as 2000 and depth as 3

testRF(vara_mean_rf,varb_mean_rf)

(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
Log Loss cv: 0.16118368189224744
Log Loss train: 0.1295171475728342
Log Loss test: 0.17283674779043046
-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) 
std_gene_var_text_hashingtfidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values

tune_randomforest(vara_hashed_rf,varb_hashed_rf)

(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
for n_estimators = 100 and max depth =  3
Log Loss cv: 1.2562525882383961
Log Loss train: 1.1495093667153717
for n_estimators = 100 and max depth =  5
Log Loss cv: 1.1414141205937414
Log Loss train: 0.9137821932641781
for n_estimators = 100 and max depth =  7
Log Loss cv: 1.0891671228800952
Log Loss train: 0.7274585518989484
for n_estimators = 100 and max depth =  10
Log Loss cv: 1.0602735362314917
Log Loss train: 0.5419761320891583
for n_estimators = 200 and max depth =  3
Log Loss cv: 1.2256031707183694
Log Loss train: 1.1125457127985048
for n_estimators = 200 and max depth =  5
Log Loss cv: 1.1239819425283584
Log Loss train: 0.8863029989503483
for n_estimators = 200 and max depth =  7
Log Loss cv: 1.0779197694234626
Log Loss train: 0.7025060768309693
for n_estimators = 200 and max depth =  10
Log Loss cv: 1.0544180795539924
Log Loss train: 0.5270883620171445
for n_estimators = 500 and max depth =  3
Log Loss cv: 1.207676966917158
Log Loss train: 1.1036352816983548
for n_estimators = 500 and max depth =  5
Log Loss cv: 1.1147682551650802
Log Loss train: 0.8732109339573471
for n_estimators = 500 and max depth =  7
Log Loss cv: 1.0736966943880142
Log Loss train: 0.6951080822600649
for n_estimators = 500 and max depth =  10
Log Loss cv: 1.0509723046279644
Log Loss train: 0.522813716084056
for n_estimators = 1000 and max depth =  3
Log Loss cv: 1.203560840318964
Log Loss train: 1.100936649316696
for n_estimators = 1000 and max depth =  5
Log Loss cv: 1.1104963322013828
Log Loss train: 0.8689259823733123
for n_estimators = 1000 and max depth =  7
Log Loss cv: 1.0701797377723286
Log Loss train: 0.6895920716141755
for n_estimators = 1000 and max depth =  10
Log Loss cv: 1.0502619913750308
Log Loss train: 0.5205027839317523
for n_estimators = 2000 and max depth =  3
Log Loss cv: 1.2024191406698308
Log Loss train: 1.1001196279920487
for n_estimators = 2000 and max depth =  5
Log Loss cv: 1.1083923280407713
Log Loss train: 0.8684530548880646
for n_estimators = 2000 and max depth =  7
Log Loss cv: 1.0670849255877433
Log Loss train: 0.687383236130885
for n_estimators = 2000 and max depth =  10
Log Loss cv: 1.049339910790633
Log Loss train: 0.518731457270126

from looking above i can take estimators as 1000 and depth as 5

testRF(vara_hashed_rf,varb_hashed_rf)

(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
Log Loss cv: 1.12511787673414
Log Loss train: 0.8844410482107985
Log Loss test: 1.1406196373627946

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:

invalid value encountered in true_divide


-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

4.6 Lets apply Stacking classifier

</p> </div> </div> </div>
Applying Stacking Classifer on MeanResponse Coding 
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values

smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

Models

def stackingClassifier(vara,varb):

    X = vara
    y_true = varb

    x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
    x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

    print ( x_train.shape,y_train.shape)
    print( x_cv.shape,y_cv.shape)
    print(x_test.shape,y_test.shape)


    neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
    neigh.fit(x_train, y_train)
    sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
    sig_clf0.fit(x_train,y_train)

    model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
    model.fit(x_train , y_train)
    sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
    sig_clf1.fit(x_train,y_train)


    clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
    clf_SGD_test.fit(x_train,y_train)
    sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
    sig_clf2.fit(x_train,y_train)

    meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)


    sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
    sF.fit(x_train, y_train)
    

    log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
    print("Log loss (train) on the stacking classifier :",log_error)
    log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
    print("Log loss (cv) on the stacking classifier :",log_error)
    log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
    print("Log loss (train) on the stacking classifier :",log_error)
    
    plot_confusion_matrix(y_test , sF.predict(x_test))

    

stackingClassifier(xres,yres)

(5390, 1018) (5390,)
(1348, 1018) (1348,)
(1685, 1018) (1685,)
Log loss (train) on the stacking classifier : 0.14030289465971651
Log loss (cv) on the stacking classifier : 0.2420519229697048
Log loss (train) on the stacking classifier : 0.25972844103712256
-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

def stackingClassifier_tunning(vara,varb):

    X = vara
    y_true = varb

    x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
    x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)

    print ( x_train.shape,y_train.shape)
    print( x_cv.shape,y_cv.shape)
    print(x_test.shape,y_test.shape)

    hp=[0.0001,0.001,0.01,0.1,1,10,15]
    logLoss_train=[]
    logLoss_test=[]
    logLoss_cv=[]
                
    for i in hp:
        neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
        neigh.fit(x_train, y_train)
        sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
        sig_clf0.fit(x_train,y_train)

        model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
        model.fit(x_train , y_train)
        sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
        sig_clf1.fit(x_train,y_train)


        clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
        clf_SGD_test.fit(x_train,y_train)
        sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
        sig_clf2.fit(x_train,y_train)

        meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)


        sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
        sF.fit(x_train, y_train)
        

        logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
        logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
        #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))

    
    plt.plot(np.log(hp), logLoss_train, label='Train logloss')
    plt.plot(np.log(hp), logLoss_cv, label='CV logloss')

    plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
    plt.legend()

    plt.xlabel("C:hyperparameter")
    plt.ylabel("LogLoss")
    plt.title("ERROR PLOTS")
    return(plt.show())
        

stackingClassifier_tunning(xres,yres)

(5390, 1018) (5390,)
(1348, 1018) (1348,)
(1685, 1018) (1685,)

for meta classifier i can take my c values as 0.1

Applyin stacking for onehotfeatures 

x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values

smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

reusing above functions

im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.

stackingClassifier_tunning(xres,yres)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-4ee702f64944> in <module>()
----> 1 stackingClassifier_tunning(xres,yres)

NameError: name 'stackingClassifier_tunning' is not defined

taking c as 1

stackingClassifier(xres,yres)

(5376, 4260) (5376,)
(1344, 4260) (1344,)
(1681, 4260) (1681,)
Log loss (train) on the stacking classifier : 0.30903590626810395
Log loss (cv) on the stacking classifier : 0.5317070749588001
Log loss (train) on the stacking classifier : 0.5271830152079766
-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

taking c as 0.1

stackingClassifier(xres,yres)

(5376, 4260) (5376,)
(1344, 4260) (1344,)
(1681, 4260) (1681,)
Log loss (train) on the stacking classifier : 0.31039276656264053
Log loss (cv) on the stacking classifier : 0.5340512149631327
Log loss (train) on the stacking classifier : 0.5269476489328632
-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

Applyin stacking for hashed Featurisation 

std_gene_var_text_hashingtfidf
0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class
0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1
1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2
3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3
4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4
3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1
3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1
3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4
3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4

3321 rows × 1019 columns

x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values

smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.

/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:

Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.


stackingClassifier_tunning(xres,yres)

(5358, 1018) (5358,)
(1340, 1018) (1340,)
(1675, 1018) (1675,)

from above figure i will select c as 0.1

stackingClassifier(xres,yres)

(5358, 1018) (5358,)
(1340, 1018) (1340,)
(1675, 1018) (1675,)
Log loss (train) on the stacking classifier : 0.31306871841068806
Log loss (cv) on the stacking classifier : 0.5266414657786868
Log loss (train) on the stacking classifier : 0.5249250625775747
-------------------- Confusion matrix --------------------

-------------------- Recall matrix (Row sum=1) --------------------

-------------------- Precision matrix (Columm Sum=1) --------------------

5.0 Final Observations Preetytableformat

</p> </div> </div> </div> 
from prettytable import PrettyTable
table=PrettyTable()

table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]

table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])

table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])

#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")

table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])


table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])


table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])


table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])



table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])


print(table)

+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
|     Feature Name    | Featurisation Used |         Model          |              HyperParameter             |  train Logloss |  Cv Logloss | test Logloss |
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
|         Text        |       TFIDF        |        Logistic        |                  0.001                  |     0.944      |     1.21    |    1.153     |
|         Text        |        BOW         |        Logistic        |                  0.001                  |     0.906      |    1.544    |    1.214     |
| gene,text,variation |      Hashing       |   StackingClassifier   | (0.1 Metaclassifier-LogisticRegression) |     0.313      |    0.526    |    0.526     |
| gene,text,variation |   OneHotEncoding   |   StackingClassifier   | (0.1 Metaclassifier-LogisticRegression) |      0.31      |    0.534    |    0.526     |
| gene,text,variation |   OneHotEncoding   |   StackingClassifier   |  (1 Metaclassifier-LogisticRegression)  |     0.309      |    0.531    |    0.527     |
| gene,text,variation | MeanResponseCoding |   StackingClassifier   | (0.1 Metaclassifier-LogisticRegression) |      0.14      |    0.242    |    0.259     |
| gene,text,variation |      Hashing       | RandomForestClassifier |        (estimatores-1000,depth-5)       |     0.884      |    1.125    |     1.14     |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier |        (estimatores-2000,depth-3)       |     0.129      |    0.161    |    0.172     |
| gene,text,variation |   onehotencoding   | RandomForestClassifier |        (estimatores-2000,depth-5)       |      0.9       |    1.117    |    1.147     |
| gene,text,variation |      Hashing       |     SVM Classifier     |                    1                    |     0.972      |    1.133    |    1.203     |
| gene,text,variation | MeanResponseCoding |     SVM Classifier     |                    1                    |     0.878      |    1.104    |    1.057     |
| gene,text,variation | MeanResponseCoding |     SVM Classifier     |                   0.1                   |     0.936      |    1.151    |    1.168     |
| gene,text,variation | MeanResponseCoding |   LogisticRegression   |                    1                    |     0.873      |    1.192    |    1.199     |
| gene,text,variation |      Hashing       |   StackingClassifier   | (0.1 Metaclassifier-LogisticRegression) |     0.313      |    0.526    |    0.526     |
| gene,text,variation |   OneHotEncoding   |   StackingClassifier   | (0.1 Metaclassifier-LogisticRegression) |      0.31      |    0.534    |    0.526     |
| gene,text,variation |   OneHotEncoding   |   StackingClassifier   |  (1 Metaclassifier-LogisticRegression)  |     0.309      |    0.531    |    0.527     |
| gene,text,variation | MeanResponseCoding |   StackingClassifier   | (0.1 Metaclassifier-LogisticRegression) |      0.14      |    0.242    |    0.259     |
| gene,text,variation |      Hashing       | RandomForestClassifier |        (estimatores-1000,depth-5)       |     0.884      |    1.125    |     1.14     |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier |        (estimatores-2000,depth-3)       |     0.129      |    0.161    |    0.172     |
| gene,text,variation |   onehotencoding   | RandomForestClassifier |        (estimatores-2000,depth-5)       |      0.9       |    1.117    |    1.147     |
| gene,text,variation |      Hashing       |     SVM Classifier     |                    1                    |     0.972      |    1.133    |    1.203     |
| gene,text,variation | MeanResponseCoding |     SVM Classifier     |                    1                    |     0.878      |    1.104    |    1.057     |
| gene,text,variation | MeanResponseCoding |     SVM Classifier     |                   0.1                   |     0.936      |    1.151    |    1.168     |
| gene,text,variation |       Hashed       |   LogisticRegression   |                    1                    |     0.873      |    1.192    |    1.199     |
| gene,text,variation | MeanResponseCoding |   LogisticRegression   |                    1                    |     0.584      |    0.857    |    0.821     |
| gene,text,variation |   onehotencoding   |   LogisticRegression   |                    1                    |     0.785      |    1.196    |    1.748     |
| gene,text,variation |       hashed       |     KNN Clasifier      |                    15                   |     0.647      |    0.751    |    0.736     |
| gene,text,variation | MeanResponseCoding |     KNN Clasifier      |                    31                   |     0.681      |    0.701    |    0.777     |
| gene,text,variation |   onehotencoding   |     KNN Clasifier      |                    31                   |     0.813      |    0.878    |    0.881     |
| gene,text,variation | MeanResponseCoding |      NB Clasifier      |                    1                    |     1.038      |    1.261    |    1.232     |
| gene,text,variation | MeanResponseCoding |      NB Clasifier      |                    1                    |     0.963      |    1.192    |    1.198     |
| gene,text,variation |      Hashing       |   StackingClassifier   | (0.1 Metaclassifier-LogisticRegression) |     0.313      |    0.526    |    0.526     |
| gene,text,variation |   OneHotEncoding   |   StackingClassifier   | (0.1 Metaclassifier-LogisticRegression) |      0.31      |    0.534    |    0.526     |
| gene,text,variation |   OneHotEncoding   |   StackingClassifier   |  (1 Metaclassifier-LogisticRegression)  |     0.309      |    0.531    |    0.527     |
| gene,text,variation | MeanResponseCoding |   StackingClassifier   | (0.1 Metaclassifier-LogisticRegression) |      0.14      |    0.242    |    0.259     |
| gene,text,variation |      Hashing       | RandomForestClassifier |        (estimatores-1000,depth-5)       |     0.884      |    1.125    |     1.14     |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier |        (estimatores-2000,depth-3)       |     0.129      |    0.161    |    0.172     |
| gene,text,variation |   onehotencoding   | RandomForestClassifier |        (estimatores-2000,depth-5)       |      0.9       |    1.117    |    1.147     |
| gene,text,variation |      Hashing       |     SVM Classifier     |                    1                    |     0.972      |    1.133    |    1.203     |
| gene,text,variation | MeanResponseCoding |     SVM Classifier     |                    1                    |     0.878      |    1.104    |    1.057     |
| gene,text,variation | MeanResponseCoding |     SVM Classifier     |                   0.1                   |     0.936      |    1.151    |    1.168     |
| gene,text,variation |       Hashed       |   LogisticRegression   |                    1                    |     0.873      |    1.192    |    1.199     |
| gene,text,variation | MeanResponseCoding |   LogisticRegression   |                    1                    |     0.584      |    0.857    |    0.821     |
| gene,text,variation |   onehotencoding   |   LogisticRegression   |                    1                    |     0.785      |    1.196    |    1.748     |
| gene,text,variation |       hashed       |     KNN Clasifier      |                    15                   |     0.647      |    0.751    |    0.736     |
| gene,text,variation | MeanResponseCoding |     KNN Clasifier      |                    31                   |     0.681      |    0.701    |    0.777     |
| gene,text,variation |   onehotencoding   |     KNN Clasifier      |                    31                   |     0.813      |    0.878    |    0.881     |
| gene,text,variation | MeanResponseCoding |      NB Clasifier      |                    1                    |     1.038      |    1.261    |    1.232     |
| gene,text,variation | MeanResponseCoding |      NB Clasifier      |                    1                    |     0.963      |    1.192    |    1.198     |
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+

Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.

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